diff --git a/DESCRIPTION b/DESCRIPTION
index 7e953a3..c17a71b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -7,11 +7,12 @@ Authors@R:
            role = c("aut", "cre"),
            email = "tiagoolivoto@gmail.com",
            comment = c(ORCID = "0000-0002-0241-9636"))
-Description: Provides tools for image manipulation and plant image
-    analysis that will help you to segment images, create binary images,
-    measure leaf area, quantify disease severity, count the number of
-    disease lesions, and obtain statistics of image objects such as
-    grains, pods, pollen, leaves and more.
+Description: Provides tools for image manipulation that will help you to
+    quantify plant leaf area, disease severity, number of disease lesions,
+    and obtain statistics of image objects such as grains, pods, pollen,
+    leaves, and more. Tools for segment images and create binary images
+    using the method of automatic threshold selection method proposed by
+    Otsu (1979) <doi:10.1109/tsmc.1979.4310076> are also provided.
 License: GPL (>= 3)
 Depends: 
     R (>= 4.0.0)
diff --git a/NAMESPACE b/NAMESPACE
index d9506fa..6372a77 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -20,7 +20,7 @@ export(image_horizontal)
 export(image_hreflect)
 export(image_import)
 export(image_index)
-export(image_pallete)
+export(image_palette)
 export(image_pliman)
 export(image_resize)
 export(image_rotate)
@@ -52,6 +52,7 @@ importFrom(parallel,makeCluster)
 importFrom(parallel,parLapply)
 importFrom(parallel,stopCluster)
 importFrom(stats,aggregate)
+importFrom(stats,as.formula)
 importFrom(stats,binomial)
 importFrom(stats,glm)
 importFrom(stats,kmeans)
diff --git a/R/color_palette.R b/R/color_palette.R
deleted file mode 100644
index e98c581..0000000
--- a/R/color_palette.R
+++ /dev/null
@@ -1,5 +0,0 @@
-color_pal <- function(n = 20, color, trim_upp, trim_low){
-  pal <- hcl.colors(20, "Blues")[-c(1:2)]
-  image_show(pal)
-  as.matrix(t(col2rgb(pal)))
-}
diff --git a/R/count_lesions.R b/R/count_lesions.R
index 648dc2e..dea1f3a 100644
--- a/R/count_lesions.R
+++ b/R/count_lesions.R
@@ -85,7 +85,12 @@
 #'   processing, when `save_image = TRUE`, the processed image will be also
 #'   saved in such a directory.
 #' @param verbose If `TRUE` (default) a summary is shown in the console.
-#' @return A data frame with the results for each image.
+#' @return A list with the following objects:
+#'  * `results` A data frame with the results (area, perimeter, radius) for
+#'  object.
+#'  * `statistics` A data frame with the summary statistics for the image.
+#'  * `count` (If `img_pattern` is used), summarizing the count number for each
+#'  image.
 #' @export
 #' @md
 #' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
@@ -96,6 +101,7 @@
 #' healthy <- image_import(image_pliman("sev_healthy.jpg"))
 #' lesions <- image_import(image_pliman("sev_sympt.jpg"))
 #' image_combine(img, healthy, lesions, ncol = 3)
+#' a <-
 #' count_lesions(img = img,
 #'               img_healthy = healthy,
 #'               img_lesion = lesions,
@@ -224,18 +230,6 @@ count_lesions <- function(img,
                  nmask <- watershed(distmap(plant_symp),
                                     tolerance = tol,
                                     ext = ext))
-          # shape <-
-          #   cbind(data.frame(computeFeatures.shape(nmask)),
-          #         data.frame(computeFeatures.moment(nmask))[,1:2]
-          #   )
-          # ifelse(!is.null(lower_size),
-          #        shape <- shape[shape$s.area > lower_size, ],
-          #        shape <- shape[shape$s.area > mean(shape$s.area) * 0.1, ])
-          # if(!is.null(upper_size)){
-          #   shape <- shape[shape$s.area < upper_size, ]
-          # }
-          # shape$id <- 1:nrow(shape)
-          # shape <- shape[, c(9, 7, 8, 1, 2:6)]
           if(show_original == TRUE & show_segmentation == FALSE){
             im2 <- img
             im2@.Data[,,1][!ID] <- col_lesions[1]
@@ -292,14 +286,12 @@ count_lesions <- function(img,
                   sintoma$df_in[sample(1:nrow(sintoma$df_in)),][1:nrows,],
                   fundo$df_in[sample(1:nrow(fundo$df_in)),][1:nrows,]) %>%
             transform(Y = ifelse(CODE == "img_background", 0, 1))
-          # fundo_resto$CODE <- NULL
           modelo1 <-
             glm(Y ~ R + G + B, family = binomial("logit"), data = fundo_resto) %>%
             suppressWarnings()
           pred1 <- predict(modelo1, newdata = original$df_in, type="response") %>% round(0)
           plant_background <- matrix(pred1, ncol = ncol(original$R))
           plant_background <- image_correct(plant_background, perc = 0.009)
-          # image_show(plant_background)
           plant_background[plant_background == 1] <- 2
           sadio_sintoma <-
             rbind(sadio$df_in[sample(1:nrow(sadio$df_in)),][1:nrows,],
@@ -318,7 +310,6 @@ count_lesions <- function(img,
           leaf_sympts <- matrix(pred3, ncol = ncol(original$R))
           leaf_sympts <- 1 - image_correct(leaf_sympts, perc = 0.009)
           plant_background[leaf_sympts == 1] <- 3
-          # mpred1 <- bwlabel(leaf_sympts == 0)
           parms <- read.csv(file=system.file("parameters.csv", package = "pliman", mustWork = TRUE),
                             header = T, sep = ";")
           parms2 <- parms[parms$object_size == lesion_size,]
@@ -333,18 +324,6 @@ count_lesions <- function(img,
                  nmask <- watershed(distmap(leaf_sympts),
                                     tolerance = tol,
                                     ext = ext))
-          # shape <-
-          #   cbind(data.frame(computeFeatures.shape(nmask)),
-          #         data.frame(computeFeatures.moment(nmask))[,1:2]
-          #   )
-          # ifelse(!is.null(lower_size),
-          #        shape <- shape[shape$s.area > lower_size, ],
-          #        shape <- shape[shape$s.area > mean(shape$s.area) * 0.1, ])
-          # if(!is.null(upper_size)){
-          #   shape <- shape[shape$s.area < upper_size, ]
-          # }
-          # shape$id <- 1:nrow(shape)
-          # shape <- shape[, c(9, 7, 8, 1, 2:6)]
           if(show_original == TRUE & show_segmentation == TRUE){
             im2 <- colorLabels(nmask)
             if(backg){
@@ -413,9 +392,6 @@ count_lesions <- function(img,
             eval(parse(text=x))}))
         ext <- ifelse(is.null(extension),  parms2[rowid, 3], extension)
         tol <- ifelse(is.null(tolerance), parms2[rowid, 4], tolerance)
-        # print(res)
-        # print(ext)
-        # print(tol)
         nmask <- watershed(distmap(img2),
                            tolerance = tol,
                            ext = ext)
@@ -464,8 +440,6 @@ count_lesions <- function(img,
         }
       }
 
-
-
       shape <-
         cbind(data.frame(computeFeatures.shape(nmask)),
               data.frame(computeFeatures.moment(nmask))[,1:2]
@@ -478,7 +452,6 @@ count_lesions <- function(img,
       }
       shape$id <- 1:nrow(shape)
       shape <- shape[, c(9, 7, 8, 1, 2:6)]
-
       show_mark <- !is.null(marker) && show_segmentation == TRUE | show_segmentation == FALSE
       marker <- ifelse(is.null(marker), "point", marker)
       marker_col <- ifelse(is.null(marker_col), "white", marker_col)
@@ -553,8 +526,12 @@ count_lesions <- function(img,
                                  NA)) %>%
         transform(statistics = c("n", "min", "mean", "max", "sd", "sum", "prop"))
       stats <- stats[c(3, 1, 2)]
+      shape <- shape[,c(1:6, 8:9, 7)]
+      colnames(shape) <- c("id", "x", "y", "area", "perimeter", "radius_mean",
+                           "radius_min", "radius_max", "radius_sd")
       results <- list(results = shape,
                       statistics = stats)
+      class(results) <- "plm_count"
       if(verbose == TRUE){
         print(results$statistics, row.names = FALSE)
       }
diff --git a/R/count_objects.R b/R/count_objects.R
index dfa19bc..2843908 100644
--- a/R/count_objects.R
+++ b/R/count_objects.R
@@ -95,7 +95,12 @@
 #'   processing, when `save_image = TRUE`, the processed image will be also
 #'   saved in such a directory.
 #' @param verbose If `TRUE` (default) a summary is shown in the console.
-#' @return A data frame with the results for each image.
+#' @return A list with the following objects:
+#'  * `results` A data frame with the results (area, perimeter, radius) for
+#'  object.
+#'  * `statistics` A data frame with the summary statistics for the image.
+#'  * `count` (If `img_pattern` is used), summarizing the count number for each
+#'  image.
 #' @export
 #' @import EBImage
 #' @md
@@ -245,7 +250,6 @@ count_objects <- function(img,
                            ext = ext)
         ID <- which(img2 == 1)
         ID2 <- which(img2 == 0)
-        feat <- computeFeatures.moment(nmask)
       }
       backg <- !is.null(col_background)
       col_background <- col2rgb(ifelse(is.null(col_background), "white", col_background))
@@ -309,7 +313,7 @@ count_objects <- function(img,
       shape$id <- 1:nrow(shape)
       shape <- shape[, c(9, 7, 8, 1, 2:6)]
       show_mark <- !is.null(marker) && show_segmentation == TRUE | show_segmentation == FALSE
-      marker <- ifelse(is.null(marker), "point", marker)
+      marker <- ifelse(is.null(marker), "text", marker)
       marker_col <- ifelse(is.null(marker_col), "white", marker_col)
       marker_size <- ifelse(is.null(marker_size), 0.9, marker_size)
       if(show_image == TRUE){
@@ -346,16 +350,16 @@ count_objects <- function(img,
         if(marker == "text"){
           marker_size <- ifelse(is.null(marker_size), 0.75, marker_size)
           image_show(im2)
-          text(feat[,1],
-               feat[,2],
-               seq(1:nrow(feat)),
+          text(shape[,2],
+               shape[,3],
+               shape[,1],
                col = marker_col,
                cex = marker_size)
         } else{
           marker_size <- ifelse(is.null(marker_size), 0.75, marker_size)
           image_show(im2)
-          points(feat[,1],
-                 feat[,2],
+          text(shape[,2],
+               shape[,3],
                  col = marker_col,
                  pch = 16,
                  cex = marker_size)
diff --git a/R/symptomatic_area.R b/R/symptomatic_area.R
index 79ae821..0b6ab7b 100644
--- a/R/symptomatic_area.R
+++ b/R/symptomatic_area.R
@@ -44,7 +44,8 @@
 #'   processing, when `save_image = TRUE`, the processed image will be also
 #'   saved in such a directory.
 #' @param verbose If `TRUE` (default) a summary is shown in the console.
-#' @return A data frame with the results for each image.
+#' @return A data frame with the results (healthy and symptomatic area) for each
+#'   image.
 #' @export
 #' @md
 #' @importFrom stats binomial glm predict kmeans sd aggregate
diff --git a/R/utils_file.R b/R/utils_file.R
index 94cfe89..d7cbabf 100644
--- a/R/utils_file.R
+++ b/R/utils_file.R
@@ -29,9 +29,23 @@
 #' @param remove_original Remove original files after manipulation? defaults to
 #'   `FALSE`. If `TRUE` the files in `pattern` will be removed.
 #' @param verbose If `FALSE`, the code is run silently.
+#' @return
+#' * `file_extension()`,  `file_name()`, and `file_dir()` return a character
+#' string.
+#' * `manipulate_files()` No return value. If `verbose == TRUE`, a message is
+#' printed indicating which operation succeeded (or not) for each of the files
+#' attempted.
 #' @export
 #' @examples
 #' \donttest{
+#' library(pliman)
+#' # get file name, directory and extension
+#' file <- "E:/my_folder/my_subfolder/image1.png"
+#' file_dir(file)
+#' file_name(file)
+#' file_extension(file)
+#'
+#' # manipulate files
 #' dir <- tempdir()
 #' list.files(dir)
 #' file.create(paste0(dir, "/test.txt"))
diff --git a/R/utils_imagem.R b/R/utils_imagem.R
index 405f12e..7dc69f0 100644
--- a/R/utils_imagem.R
+++ b/R/utils_imagem.R
@@ -535,9 +535,9 @@ image_binary <- function(image,
   }
 }
 
-#' Image indices
+#' Image indexes
 #'
-#' Builds image indices using Red, Green, Blue, Red-Edge, and NIR bands.
+#' Builds image indexes using Red, Green, Blue, Red-Edge, and NIR bands.
 #' @details
 #' The following indexes are available in pliman.
 #'
@@ -992,14 +992,17 @@ image_to_mat <- function(image,
 #' @param workers A positive numeric scalar or a function specifying the maximum
 #'   number of parallel processes that can be active at the same time.
 #' @param verbose If `TRUE` (default) a summary is shown in the console.
+#' @return A list with `npal` color palettes of class `Image`.
 #' @export
 #' @examples
+#' \donttest{
 #' library(pliman)
 #'img <- image_import(image_pliman("sev_leaf.jpg"))
-#'pal <- image_pallete(img, 2)
+#'pal <- image_palette(img, 2)
 #'image_show(pal[[1]])
 #'image_show(pal[[2]])
-image_pallete <- function(image,
+#'}
+image_palette <- function(image,
                           npal,
                           nstart = 25,
                           parallel = FALSE,
@@ -1017,9 +1020,9 @@ image_pallete <- function(image,
       if(verbose == TRUE){
         message("Image processing using multiple sessions (",nworkers, "). Please wait.")
       }
-      parLapply(clust, image, image_pallete, npal, nstart)
+      parLapply(clust, image, image_palette, npal, nstart)
     } else{
-      lapply(image, image_pallete, npal, nstart)
+      lapply(image, image_palette, npal, nstart)
     }
   } else{
   df <- image_to_mat(image)$df_in
@@ -1032,7 +1035,7 @@ image_pallete <- function(image,
     R <- df1[1:dim_mat^2, 1]
     G <- df1[1:dim_mat^2, 2]
     B <- df1[1:dim_mat^2, 3]
-    rgbs[[i]] <- array(c(R, G, B), dim = c(dim_mat, dim_mat, 3))
+    rgbs[[i]] <- as.Image(array(c(R, G, B), dim = c(dim_mat, dim_mat, 3)))
   }
   return(rgbs)
   }
@@ -1048,7 +1051,8 @@ image_pallete <- function(image,
 #' @param dpi The image resolution in dots per inch.
 #' @param px The number of pixels.
 #' @param cm The size in centimeters.
-#' @return A numerical value.
+#' @return A numeric value or a vector of numeric values if the input data is a
+#'   vector
 #' @export
 #' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
 #' @examples
diff --git a/R/utils_measures.R b/R/utils_measures.R
index e80387b..cc23c94 100644
--- a/R/utils_measures.R
+++ b/R/utils_measures.R
@@ -1,6 +1,6 @@
 #' Utilities for object measures
 #'
-#'* `get_measures()` Computes object measures (area, perimeter, radius) by using
+#'* `get_measures()` computes object measures (area, perimeter, radius) by using
 #'either a known resolution (dpi) or an object with known measurements.
 #' * `plot_measures()` draws the object measures given in an object to the
 #' current plot. The object identification (`"id"`) is drawn by default.
@@ -9,32 +9,33 @@
 #' @param object An object computed with [count_objects()] or [leaf_area()].
 #' @param dpi A known resolution of the image in DPI (dots per inch).
 #' @param id An object in the image to indicate a known value.
-#' @param measure The measure of the known object. One of the following:
-#' * `"area"` The known area of the object.
-#' * `"perimeter"` The known perimeter of the object.
-#' * `"radius_mean"` The known radius of the object.
-#' * `"radius_min"` The known minimum radius of the object. If the object is a
+#' @param measure  A two-sided formula, e.g., `measure = area ~ 100` indicating
+#'   the known value of object `id`. The right-hand side is the known value and
+#'   the left-hand side can be one of the following.
+#' * `area` The known area of the object.
+#' * `perimeter` The known perimeter of the object.
+#' * `radius_mean` The known radius of the object.
+#' * `radius_min` The known minimum radius of the object. If the object is a
 #' square, then the `radius_min` of such object will be `L/2` where `L` is the
 #' length of the square side.
-#' * `"radius_max"` The known maximum radius of the object. If the object is a
+#' * `radius_max` The known maximum radius of the object. If the object is a
 #' square, then the `radius_max` of such object according to the Pythagorean
 #' theorem will be `L x sqrt(2) / 2` where `L` is the length of the square side.
-#' @param true_value The know value of the object `id.`
 #' @param digits The number of significant figures. Defaults to `2.`
 #' @param size The size of the text. Defaults to `0.9`.
 #' @param col The color of the text. Defaults to `"white"`.
 #' @param verbose If `FALSE`, runs the code silently.
 #' @param ... Further arguments passed on to [graphics::text()].
 #' @return
-#' * `get_measures()` returns a data frame with the object `id` and the measures. If any of the
-#'   `"area"`, `"perimeter"`, `"radius_mean"`, `"radius_min"` or `"radius_max"`
-#'   is informed, the informed measure will be returned in the unit given in
-#'   `true_value` and the other measures in pixels. If `dpi` is informed, then
-#'   all the measures will be adjusted to the known `dpi` and the values
-#'   returned in cm2 (area) and cm (the other measures).
+#' * `get_measures()` returns a data frame with the object `id` and the
+#' measures. If `measure` is informed, the pixel values will be corrected by the
+#' value of the known object, given in the unit of the right-hand side of
+#' `measure`. If `dpi` is informed, then all the measures will be adjusted to
+#' the known `dpi`.
 #' * `plot_measures()` returns a `NULL` object, drawing the text according to
 #' the x and y coordinates of the objects in `object`.
 #' @export
+#' @importFrom stats as.formula
 #' @examples
 #' \donttest{
 #' library(pliman)
@@ -62,17 +63,15 @@
 #' measures[1, "radius_max"] * 2
 #'
 #'
-#' # Get object measures by declaring the known area.
+#' # Get object measures by declaring the known area of object 1
 #' get_measures(results,
 #'              id = 1,
-#'              measure = "area",
-#'              true_value = 100)
+#'              area ~ 100)
 #'}
 get_measures <- function(object,
-                         dpi = NULL,
                          id = NULL,
                          measure = NULL,
-                         true_value = NULL,
+                         dpi = NULL,
                          verbose = TRUE){
   if(is.data.frame(object)){
     if(any(c("area", "perimeter", "radius_mean") %in% colnames(object) == FALSE)){
@@ -81,7 +80,7 @@ get_measures <- function(object,
     res <- object
   }
   if(any(class(object) == "plm_count")){
-  res <- object$results
+    res <- object$results
   }
   if(any(class(object) == "plm_la")){
     obj <- as.numeric(class(object)[[3]])
@@ -94,7 +93,7 @@ get_measures <- function(object,
     }
   }
   if(any(class(object) == "objects_rgb")){
-  res <- object[["objects"]]
+    res <- object[["objects"]]
   }
   if(!is.null(id) & !is.null(dpi)){
     stop("Only one of 'dpi' or 'id' can be used.", call. = FALSE)
@@ -102,22 +101,41 @@ get_measures <- function(object,
   if(!is.null(id) & is.null(measure) ){
     stop("'measure' must be informed.", call. = FALSE)
   }
-  if(!is.null(id) &  is.null(true_value)){
-    stop("'true_value' must be informed.", call. = FALSE)
-  }
   if(!is.null(id)){
+    if(class(measure) != "formula"){
+      stop("'measure' must be a two-sided formula, e.g., 'area ~ 25'.")
+    }
+    terms <- as.formula(measure)
+    var <- as.character(terms[[2]])
+    value <- as.numeric(terms[[3]])
     measures <- c("area", "perimeter", "radius_mean", "radius_min",  "radius_max")
-    if(!measure %in% measures){
-      stop("'measure' must be one of ", paste(measures, collapse = ", "), call. = FALSE)
+    if(!var %in% measures){
+      stop("The left-hand side of 'measure' must be one of ", paste(measures, collapse = ", "), call. = FALSE)
+    }
+    if(var == "area"){
+      id_val <- res[which(res$id == id), var]
+      px_side <- sqrt(value / id_val)
+      values <- res[, var]
+      corrected <- values * value / id_val
+      res$area <- corrected
+      res$perimeter <- res$perimeter * px_side
+      res$radius_mean <- res$radius_mean * px_side
+      res$radius_min <- res$radius_min * px_side
+      res$radius_max <- res$radius_max * px_side
+    }
+    if(var != "area"){
+      id_val <- res[which(res$id == id), var]
+      px_side <- value / id_val
+      res$area <- res$area * px_side^2
+      res$perimeter <- res$perimeter * px_side
+      res$radius_mean <- res$radius_mean * px_side
+      res$radius_min <- res$radius_min * px_side
+      res$radius_max <- res$radius_max * px_side
     }
-    id_val <- res[which(res$id == id), measure]
-    values <- res[, measure]
-    corrected <- values * true_value / id_val
-    res[which(colnames(res) == measure)] <- corrected
     if(verbose == TRUE){
-    cat("-----------------------------------------\n")
-    cat(paste0("measures corrected with:\nobject id: ", id, "\n", measure, ": ",  true_value, "\n"))
-    cat("-----------------------------------------\n")
+      cat("-----------------------------------------\n")
+      cat(paste0("measures corrected with:\nobject id: ", id, "\n", var, ": ",  value, "\n"))
+      cat("-----------------------------------------\n")
     }
   }
   if(!is.null(dpi)){
diff --git a/cran-comments.md b/cran-comments.md
index 1279e17..5992fe8 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,5 +1,32 @@
 ## Release summary
-This is the first submission of `pliman` (0.1.0) to CRAN
+This is a resubmission of the `pliman` (0.1.0) that includes the suggestions given by Julia Haider in the first submission of the package to CRAN
+
+## Suggestions
+
+If there are references describing the methods in your package, please
+add these in the description field of your DESCRIPTION file in the form
+authors (year) <doi:...>
+authors (year) <arXiv:...>
+authors (year, ISBN:...)
+or if those are not available: <https:...>
+with no space after 'doi:', 'arXiv:', 'https:' and angle brackets for
+auto-linking.
+(If you want to add a title as well please put it in quotes: "Title")
+
+* Implemented. Please, see the DESCRIPTION file.
+
+
+Please add \value to .Rd files regarding exported methods and explain
+the functions results in the documentation. Please write about the
+structure of the output (class) and also what the output means. (If a
+function does not return a value, please document that too, e.g.
+\value{No return value, called for side effects} or similar)
+Missing Rd-tags:
+      image_pallete.Rd: \value
+      utils_file.Rd: \value
+      
+* Done! I have included \value to all the .Rd files.
+      
 
 ## Test environments
 * local R installation, R 4.0.5
diff --git a/man/count_lesions.Rd b/man/count_lesions.Rd
index 66d1ebc..c02223f 100644
--- a/man/count_lesions.Rd
+++ b/man/count_lesions.Rd
@@ -149,7 +149,14 @@ saved in such a directory.}
 \item{verbose}{If \code{TRUE} (default) a summary is shown in the console.}
 }
 \value{
-A data frame with the results for each image.
+A list with the following objects:
+\itemize{
+\item \code{results} A data frame with the results (area, perimeter, radius) for
+object.
+\item \code{statistics} A data frame with the summary statistics for the image.
+\item \code{count} (If \code{img_pattern} is used), summarizing the count number for each
+image.
+}
 }
 \description{
 Counts the number of lesions in a sample or entire leaf based on provided
@@ -168,6 +175,7 @@ img <- image_import(image_pliman("sev_leaf_nb.jpg"))
 healthy <- image_import(image_pliman("sev_healthy.jpg"))
 lesions <- image_import(image_pliman("sev_sympt.jpg"))
 image_combine(img, healthy, lesions, ncol = 3)
+a <-
 count_lesions(img = img,
               img_healthy = healthy,
               img_lesion = lesions,
diff --git a/man/count_objects.Rd b/man/count_objects.Rd
index bf4073a..0b35915 100644
--- a/man/count_objects.Rd
+++ b/man/count_objects.Rd
@@ -148,7 +148,14 @@ saved in such a directory.}
 \item{verbose}{If \code{TRUE} (default) a summary is shown in the console.}
 }
 \value{
-A data frame with the results for each image.
+A list with the following objects:
+\itemize{
+\item \code{results} A data frame with the results (area, perimeter, radius) for
+object.
+\item \code{statistics} A data frame with the summary statistics for the image.
+\item \code{count} (If \code{img_pattern} is used), summarizing the count number for each
+image.
+}
 }
 \description{
 Counts the number of objects in an image. See more at details.
diff --git a/man/image_index.Rd b/man/image_index.Rd
index 3833a28..f16129f 100644
--- a/man/image_index.Rd
+++ b/man/image_index.Rd
@@ -3,7 +3,7 @@
 \name{image_index}
 \alias{image_index}
 \alias{plot.image_index}
-\title{Image indices}
+\title{Image indexes}
 \usage{
 image_index(
   image,
@@ -74,7 +74,7 @@ A \code{ggplot} object containing the distribution of the pixels for each
 index.
 }
 \description{
-Builds image indices using Red, Green, Blue, Red-Edge, and NIR bands.
+Builds image indexes using Red, Green, Blue, Red-Edge, and NIR bands.
 
 Produces an histogram of an \code{image_index} object
 }
diff --git a/man/image_pallete.Rd b/man/image_palette.Rd
similarity index 86%
rename from man/image_pallete.Rd
rename to man/image_palette.Rd
index 25c88ac..4d8afaf 100644
--- a/man/image_pallete.Rd
+++ b/man/image_palette.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/utils_imagem.R
-\name{image_pallete}
-\alias{image_pallete}
+\name{image_palette}
+\alias{image_palette}
 \title{Create an image palette}
 \usage{
-image_pallete(
+image_palette(
   image,
   npal,
   nstart = 25,
@@ -30,13 +30,18 @@ number of parallel processes that can be active at the same time.}
 
 \item{verbose}{If \code{TRUE} (default) a summary is shown in the console.}
 }
+\value{
+A list with \code{npal} color palettes of class \code{Image}.
+}
 \description{
 Creates image palettes by applying the k-means algorithm to the RGB values.
 }
 \examples{
+\donttest{
 library(pliman)
 img <- image_import(image_pliman("sev_leaf.jpg"))
-pal <- image_pallete(img, 2)
+pal <- image_palette(img, 2)
 image_show(pal[[1]])
 image_show(pal[[2]])
 }
+}
diff --git a/man/symptomatic_area.Rd b/man/symptomatic_area.Rd
index 6122bb0..58e9a9f 100644
--- a/man/symptomatic_area.Rd
+++ b/man/symptomatic_area.Rd
@@ -84,7 +84,8 @@ saved in such a directory.}
 \item{verbose}{If \code{TRUE} (default) a summary is shown in the console.}
 }
 \value{
-A data frame with the results for each image.
+A data frame with the results (healthy and symptomatic area) for each
+image.
 }
 \description{
 Calculates the percentage of symptomatic leaf area in sample or entire
diff --git a/man/utils_dpi.Rd b/man/utils_dpi.Rd
index 4d509b9..099f3e8 100644
--- a/man/utils_dpi.Rd
+++ b/man/utils_dpi.Rd
@@ -24,7 +24,8 @@ cm_to_pixels(cm, dpi)
 \item{px}{The number of pixels.}
 }
 \value{
-A numerical value.
+A numeric value or a vector of numeric values if the input data is a
+vector
 }
 \description{
 Provides useful conversions between size (cm), number of pixels (px) and
diff --git a/man/utils_file.Rd b/man/utils_file.Rd
index abd9658..ee5fa00 100644
--- a/man/utils_file.Rd
+++ b/man/utils_file.Rd
@@ -60,6 +60,15 @@ Set \code{overwrite = TRUE} to overwrite the files.}
 
 \item{verbose}{If \code{FALSE}, the code is run silently.}
 }
+\value{
+\itemize{
+\item \code{file_extension()},  \code{file_name()}, and \code{file_dir()} return a character
+string.
+\item \code{manipulate_files()} No return value. If \code{verbose == TRUE}, a message is
+printed indicating which operation succeeded (or not) for each of the files
+attempted.
+}
+}
 \description{
 \itemize{
 \item \code{file_extension()} Get the extension of a file.
@@ -72,6 +81,14 @@ directory.
 }
 \examples{
 \donttest{
+library(pliman)
+# get file name, directory and extension
+file <- "E:/my_folder/my_subfolder/image1.png"
+file_dir(file)
+file_name(file)
+file_extension(file)
+
+# manipulate files
 dir <- tempdir()
 list.files(dir)
 file.create(paste0(dir, "/test.txt"))
diff --git a/man/utils_measures.Rd b/man/utils_measures.Rd
index 0ac59cd..f2ae8e8 100644
--- a/man/utils_measures.Rd
+++ b/man/utils_measures.Rd
@@ -6,14 +6,7 @@
 \alias{plot_measures}
 \title{Utilities for object measures}
 \usage{
-get_measures(
-  object,
-  dpi = NULL,
-  id = NULL,
-  measure = NULL,
-  true_value = NULL,
-  verbose = TRUE
-)
+get_measures(object, id = NULL, measure = NULL, dpi = NULL, verbose = TRUE)
 
 plot_measures(
   object,
@@ -27,24 +20,24 @@ plot_measures(
 \arguments{
 \item{object}{An object computed with \code{\link[=count_objects]{count_objects()}} or \code{\link[=leaf_area]{leaf_area()}}.}
 
-\item{dpi}{A known resolution of the image in DPI (dots per inch).}
-
 \item{id}{An object in the image to indicate a known value.}
 
-\item{measure}{The measure of the known object. One of the following:
+\item{measure}{A two-sided formula, e.g., \code{measure = area ~ 100} indicating
+the known value of object \code{id}. The right-hand side is the known value and
+the left-hand side can be one of the following.
 \itemize{
-\item \code{"area"} The known area of the object.
-\item \code{"perimeter"} The known perimeter of the object.
-\item \code{"radius_mean"} The known radius of the object.
-\item \code{"radius_min"} The known minimum radius of the object. If the object is a
+\item \code{area} The known area of the object.
+\item \code{perimeter} The known perimeter of the object.
+\item \code{radius_mean} The known radius of the object.
+\item \code{radius_min} The known minimum radius of the object. If the object is a
 square, then the \code{radius_min} of such object will be \code{L/2} where \code{L} is the
 length of the square side.
-\item \code{"radius_max"} The known maximum radius of the object. If the object is a
+\item \code{radius_max} The known maximum radius of the object. If the object is a
 square, then the \code{radius_max} of such object according to the Pythagorean
 theorem will be \verb{L x sqrt(2) / 2} where \code{L} is the length of the square side.
 }}
 
-\item{true_value}{The know value of the object \code{id.}}
+\item{dpi}{A known resolution of the image in DPI (dots per inch).}
 
 \item{verbose}{If \code{FALSE}, runs the code silently.}
 
@@ -58,19 +51,18 @@ theorem will be \verb{L x sqrt(2) / 2} where \code{L} is the length of the squar
 }
 \value{
 \itemize{
-\item \code{get_measures()} returns a data frame with the object \code{id} and the measures. If any of the
-\code{"area"}, \code{"perimeter"}, \code{"radius_mean"}, \code{"radius_min"} or \code{"radius_max"}
-is informed, the informed measure will be returned in the unit given in
-\code{true_value} and the other measures in pixels. If \code{dpi} is informed, then
-all the measures will be adjusted to the known \code{dpi} and the values
-returned in cm2 (area) and cm (the other measures).
+\item \code{get_measures()} returns a data frame with the object \code{id} and the
+measures. If \code{measure} is informed, the pixel values will be corrected by the
+value of the known object, given in the unit of the right-hand side of
+\code{measure}. If \code{dpi} is informed, then all the measures will be adjusted to
+the known \code{dpi}.
 \item \code{plot_measures()} returns a \code{NULL} object, drawing the text according to
 the x and y coordinates of the objects in \code{object}.
 }
 }
 \description{
 \itemize{
-\item \code{get_measures()} Computes object measures (area, perimeter, radius) by using
+\item \code{get_measures()} computes object measures (area, perimeter, radius) by using
 either a known resolution (dpi) or an object with known measurements.
 \item \code{plot_measures()} draws the object measures given in an object to the
 current plot. The object identification (\code{"id"}) is drawn by default.
@@ -103,10 +95,9 @@ plot_measures(results, measure = "id")
 measures[1, "radius_max"] * 2
 
 
-# Get object measures by declaring the known area.
+# Get object measures by declaring the known area of object 1
 get_measures(results,
              id = 1,
-             measure = "area",
-             true_value = 100)
+             area ~ 100)
 }
 }
diff --git a/vignettes/count_objects.Rmd b/vignettes/count_objects.Rmd
index 4b22d4a..f6be284 100644
--- a/vignettes/count_objects.Rmd
+++ b/vignettes/count_objects.Rmd
@@ -70,8 +70,7 @@ Since we have known the area of the larger square (object 1), let us adjust the
 ```{r}
 get_measures(img_res,
              id = 1,
-             measure = "area",
-             true_value = 100)
+             area ~ 100)
 ```
 
 The same can be used to adjust the measures based on the perimeter or radius. Let us adjust the perimeter of objects by the perimeter of object 2 (20 cm).
@@ -79,8 +78,7 @@ The same can be used to adjust the measures based on the perimeter or radius. Le
 ```{r}
 get_measures(img_res,
              id = 2,
-             measure = "perimeter",
-             true_value = 20)
+             perimeter ~ 20)
 ```
 
 ## Declaring the image resolution
diff --git a/vignettes/leaf_area.Rmd b/vignettes/leaf_area.Rmd
index e1b679a..bae83ed 100644
--- a/vignettes/leaf_area.Rmd
+++ b/vignettes/leaf_area.Rmd
@@ -90,8 +90,7 @@ The function `get_measures()` is used to adjust the leaf area using the object 6
 area <- 
 get_measures(count,
              id = 6,
-             measure = "area",
-             true_value = 4)
+             area ~ 4)
 area
 image_show(leaves)
 plot_measures(area, measure = "area")