Histogram equalization

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

Color image palette construction based on the HSI color system for minimizing the reconstruction error

Abstract: In transmitting color images represented by the palette, the indices corrupted by the bit error cause serious image quality degradation in the reconstructed images at a receiver. This paper proposes an ordered palette construction algorithm based on the hue-saturation-intensity (HSI) color system for minimizing the reconstruction error. We define the average Euclidean distance (AED) as a new measure for comparing the performance of color image palette construction algorithms and show the effectiveness of the proposed method in the case of the bit error. Experimental results show that the proposed algorithm effectively reduces the number of mismatched colors caused by the corrupted indices and thus the reconstruction error.

Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network.

Abstract: Conventional nighttime face detection studies mostly use near-infrared (NIR) light cameras or thermal cameras, which are robust to environmental illumination variation and low illumination. However, for the NIR camera, it is difficult to adjust the intensity and angle of the additional NIR illuminator according to its distance from an object. As for the thermal camera, it is expensive to use as a surveillance camera. For these reasons, we propose a nighttime face detection method based on deep learning using a single visible-light camera. In a long-distance night image, it is difficult to detect faces directly from the entire image due to noise and image blur. Therefore, we propose Two-Step Faster region-based convolutional neural network (R-CNN) based on the image preprocessed by histogram equalization (HE). As a two-step scheme, our method sequentially performs the detectors of body and face areas, and locates the face inside a limited body area. By using our two-step method, the processing time by Faster R-CNN can be reduced while maintaining the accuracy of face detection by Faster R-CNN. Using a self-constructed database called Dongguk Nighttime Face Detection database (DNFD-DB1) and an open database of Fudan University, we proved that the proposed method performs better compared to other existing face detectors. In addition, the proposed Two-Step Faster R-CNN outperformed single Faster R-CNN and our method with HE showed higher accuracies than those without our preprocessing in nighttime face detection.

Image enhancement based on equal area dualistic

Abstract: Histogram equalization is a simple and effective image enhancing technique. But in some conditions, the luminance of an image may be changed significantly after equalizing process, this is why it never be utilized in video system in the past. A novel histogram equalization technique, equal area dualistic sub-image histogram equalization, is put forward in this paper. First, the image is decomposed into two equal area sub-images based on its original probability density function. Then the two sub-images are equalized respectively. At last, we get the result after the processed sub-images are composed into one image. The simulation result indicates that the algorithm can not only enhance image information effectively but also keep the original image luminance well enough to make it possible to be used in video system directly.

An automatic bi-directional passing-people counting method based on color image processing

Abstract: Based on color image processing, an automatic bidirectional counting method of pedestrians through a gate is proposed. In the developed technique, one color video camera is hung from the ceiling of the gate with a directly downward view so that the passing people will be observed from just overhead. Firstly, the passing people is roughly counted with the area of people in an image. The moving direction of the pedestrian can be oriented by tracking each people pattern through analyzing its HSI histogram. With features extracted from the quantized histograms of I (intensity) or H (hue), the first counting can be refined. Experimental results show that an 100% accuracy of bidirectional counting can be achieved in the case of multiple isolated one-person patterns and the same accuracy can be also obtained unless the people number of a multiple-person pattern is over five.