Quality Determination and Grading of Tomatoes using Raspberry Pi

In India cultivation of tomatoes is carried out by traditional methods and techniques. Today tremendous improvement in field of agriculture technologies and products can be seen. The tomatoes affect the overall production drastically. Image processing technique can be key technique for finding good qualities of tomatoes and grading. This work aimed to study different types of algorithms used for quality grading and sorting of fruit from the acquire image. In previous years several types of techniques are applied to analyses the good quality fruits. A simple system can be implemented using Raspberry pi with computer vision technology and image processing algorithms

Perceptual color clustering for color image segmentation based on CIEDE2000 color distance

In this paper, a novel technique for color clustering with application to color image segmentation is presented. Clustering is performed by applying the k-means algorithm in the L*a*b* color space. Nevertheless, Euclidean distance is not the metric chosen to measure distances, but CIEDE2000 color difference formula is applied instead. K-means algorithm performs iteratively the two following steps: assigning each pixel to the nearest centroid and updating the centroids so that the empirical quantization error is minimized. In this approach, in the first step, pixels are assigned to the nearest centroid according to the CIEDE2000 color distance. The minimization of the empirical quantization error when using CIEDE2000 involves finding an absolute minimum in a non-linear equation and, therefore, an analytical solution cannot be obtained. As a consequence, a heuristic method to update the centroids is proposed. The proposed algorithm has been compared with the traditional k-means clustering algorithm in the L*a*b* color space with the Euclidean distance. The Borsotti parameter was computed for 28 color images. The new version proposed outperformed the traditional one in all cases

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods

An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation

Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical image segmentation. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold and the use of Euclidean Distance as distance measure. Such an approach leads to a weak reliability and shape matching of the produced segments. Hence, this study proposes an Improved Fast Scanning algorithm that is based on Sorensen distance measure and adaptive threshold function. The proposed adaptive threshold function is based on the grey value in an image’s pixels and variance. The proposed Improved Fast Scanning algorithm is realized on two datasets which contains images of cars and nature. Evaluation is made by calculating the Peak Signal to Noise Ratio (PSNR) for the Improved Fast Scanning and standard Fast Scanning algorithm. Experimental results showed that proposed algorithm produced higher PSNR compared to the standard Fast Scanning. Such a result indicate that the proposed Improved Fast Scanning algorithm is useful in image segmentation and later contribute in identifying region of interesting in pattern recognition

Image segmentation with adaptive region growing based on a polynomial surface model

A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L∞ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces

An Efficient Algorithm for Earth Surface Interpretation from Satellite Imagery

Many image segmentation algorithms are available but most of them are not fit for interpretation of satellite images. Mean-shift algorithm has been used in many recent researches as a promising image segmentation technique, which has the speed at O(kn2) where n is the number of data points and k is the number of average iteration steps for each data point. This method computes using a brute-force in the iteration of a pixel to compare with the region it is in. This paper proposes a novel algorithm named First-order Neighborhood Mean-shift (FNM) segmentation, which is enhanced from Mean-shift segmentation. This algorithm provides information about the relationship of a pixel with its neighbors; and makes them fall into the same region which improve the speed to O(kn). In this experiment, FNM were compared to well-known algorithms, i.e., K-mean (KM), Constrained K-mean (CKM), Adaptive K-mean (AKM), Fuzzy C-mean (FCM) and Mean-shift (MS) using the reference map from Landsat. FNM provided better results in terms of overall error and correctness criteria