Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.

Value based semi automatic segmentation of satellite images using HSV color space, histogram equalization and modified FCM clustering algorithm

Abstract: Color image segmentation is very useful in many image processing applications It is possible to identify regions of interest and objects in the scene from the segmentation results, which is very beneficial to the subsequent image analysis The main objective of the image segmentation is to simplify and change the representation of an image that is easier to analyze In the satellite image processing, the segmentation is one of the vital step for gathering information from the satellite images In this paper, an efficient and accurate method of segmentation of satellite images using HSV color space and Modified Fuzzy C Means is proposed In the HSV color space, the intensity and the color information can easily be separated Our eye is more sensitive to intensity than color information (hue or saturation) In the proposed approach, the satellite image in RGB color space is transformed into HSV color space and then the transformed satellite image is split into three different components (channels or images) based on intensity and color The value or intensity component is segmented by modified Fuzzy C Means clustering algorithm after histogram equalization is completed The proposed approach is applied to analyze the satellite images of various format and size The experimental result shows that the proposed method is efficient for extracting information from the satellite images

Speeded up robust features for efficient iris recognition

Abstract: Iris recognition system is one of the biometric systems in which the development is growing rapidly. In this paper, speeded up robust features (SURFs) are used for detecting and describing iris keypoints. For feature matching, simple fusion rules are applied at different levels. Contrast-limited adaptive histogram equalization (CLAHE) is applied on the normalized image and is compared with histogram equalization (HE) and adaptive histogram equalization (AHE). The aim is to find the best enhancement technique with SURF and to verify the necessity of iris image enhancement. The recognition accuracy in each case is calculated. Experimental results demonstrate that CLAHE is a crucial enhancement step for SURF-based iris recognition. More keypoints can be extracted with enhancement using CLAHE compared to HE and AHE. This alleviates the problem of feature loss and increases the recognition accuracy. The accuracies of recognition using left and right iris images are 99 and 99.5 %, respectively. Fusion of local distances and choosing suitable fusion rules affect the recognition accuracy, noticeably. The proposed SURF-based algorithm is compared with scale-invariant feature transform, histogram of oriented gradients, maximally stable extremal regions and DAISY. Results show that the proposed algorithm is robust to different image variations and gives the highest recognition accuracy.

A Novel Fuzzy Clustering-Based Histogram Model for Image Contrast Enhancement

Abstract: Histogram equalization is a famous method for enhancing the contrast and image features. However, in few cases, it causes the overenhancement, and hence demolishes the natural display of the image. Therefore, in this article, a new fuzzy clustering based subhistogram scheme using discrete cosine transform (DCT) for contrast enhancement has been proposed. For preserving the distinctive appearance of the image, histogram division and separate histogram equalization is done on each subhistogram. The way of dividing histogram and calculating the numbers of parts for histogram division are the major problems which directly affects the quality of the output image. The proposed fuzzy-DCT scheme includes automatic calculation of a number of parts in which histogram is divided. Histogram division has done on the basis of density function and histogram separation is computed in such a way that each main peak can be divided in a different segment. The proposed scheme consists of four stages. The first stage includes the automatic calculation of number of clusters for image brightness levels. The second stage includes clustering of brightness levels by the fuzzy c -means clustering method and utilizing the given transfer function of histogram equalization. In the third stage, contrast enhancement is computed on each individual cluster separately. In the final stage, DCT is employed on the resulting image of the third step for better contrast and brightness preservation. The simulation results of the proposed scheme reveal not only clearer features along with a contrast enhancement, but also remarkably more natural look in the images.

Citrus Tree Segmentation from UAV Images Based on Monocular Machine Vision in a Natural Orchard Environment.

Abstract: The segmentation of citrus trees in a natural orchard environment is a key technology for achieving the fully autonomous operation of agricultural unmanned aerial vehicles (UAVs). Therefore, a tree segmentation method based on monocular machine vision technology and a support vector machine (SVM) algorithm are proposed in this paper to segment citrus trees precisely under different brightness and weed coverage conditions. To reduce the sensitivity to environmental brightness, a selective illumination histogram equalization method was developed to compensate for the illumination, thereby improving the brightness contrast for the foreground without changing its hue and saturation. To accurately differentiate fruit trees from different weed coverage backgrounds, a chromatic aberration segmentation algorithm and the Otsu threshold method were combined to extract potential fruit tree regions. Then, 14 color features, five statistical texture features, and local binary pattern features of those regions were calculated to establish an SVM segmentation model. The proposed method was verified on a dataset with different brightness and weed coverage conditions, and the results show that the citrus tree segmentation accuracy reached 85.27% ± 9.43%; thus, the proposed method achieved better performance than two similar methods.

A new approach for real-time histogram equalization using FPGA

Abstract: This paper presents a novel design for real-time histogram equalization based on field programmable gate arrays (FPGAs). The design is implemented using non-conventional schemes to compute the histogram statistics and equalization in parallel. Counters are used in conjunction with a dedicated decoder specially designed for this purpose. The hardware is fast, simple, and flexible with reasonable development cost. The proposed system is implemented using Stratix II family chip type EP2S15F484C3. The maximum clock frequency can reach up to 250 MHz. In this case, the total time required to perform histogram equalization for an image of size 256 /spl times/ 256 is 0.262 ms.