Fast optical flow using 3D shortest path techniques

Optical flow or image motion estimation is important in the area of computer vision. This paper presents a fast and reliable optical flow algorithm which produces a dense optical flow map by using fast cross correlation and 3D shortest path techniques. Fast correlation is achieved by using the box-filtering technique which is invariant to the size of the correlation window. The motion for each scanline or each column of the input image is obtained from the correlation coefficient volume by finding the best 3D path using dynamic programming techniques rather than simply choosing the position that gives the maximum cross correlation coefficient. Sub-pixel accuracy is achieved by fitting the local correlation coefficients to a quadratic surface. Typical running time for a 256×256 image is in the order of a few seconds on a 85 MHz computer. A variety of synthetic and real images have been tested, and good results have been obtained.     

From binary to grey tone image processing using fuzzy logic concepts

The concept of fuzzy logic was introduced by Zadeh in 1965 as a tool for analysing complex systems and decision processes. In fuzzy set theory, the binary Boolean operations of OR and AND generalise to the min and max functions. Nakagawa(⁸) later introduced the idea of local min and max operators to image processing as the grey equivalents to the parallel binary operations of shrinking and expanding. In this paper the ideas of min and max are further investigated and their usefulness in image processing assessed by extending some already well known binary processes into grey level algorithms. The functions of min_π and max_π are introduced (deleted neighbourhood min_π and max_π) as the analogues of nearest neighbour “propagation” signals of binary images. Examples of grey edge detection, spatial filtering, object labelling and grey thinning algorithms are given.     

Real-time parallel computation of disparity and optical flow using phase difference

A phase-difference-based algorithm for disparity and optical flow estimation is implemented on a TI-C40-based parallel DSP system. The module performs real-time computation of disparity maps on images of size 128 × 128 pixels and computation of optical flows on images of size 64 × 64 pixels. This paper describes the algorithm and its parallel implementation. Processing times required for the computation of disparity maps and velocity fields and measures of the algorithm's performance are reported in detail.     

Design and use of DIP-1: A fast, flexible and dynamically microprogrammable pipelined image processor

The design of a fast, flexible and dynamically microprogrammable pipelined image processor is presented. The machine is especially suited, though not completely devoted, to perform local operations (up to 16 × 16) of both logical and arithmetic character on pictures, stored in a random access image memory in a 256 level grey scale. Separate parts of the machine take care of data manipulation and address generation. The machine's functioning is illustrated by discussing the way in which arithmetic N × N neighbourhood operations and binary 3 × 3 neighbourhood operations were implemented and finally the software supporting microprogram development and debugging and the run-time support software is described.     

An efficient method for multilevel color image thresholding using cuckoo search algorithm based on minimum cross entropy

Among various thresholding methods, minimum cross entropy is implemented for its effectiveness and simplicity. Although it is efficient and gives excellent result in case of bi-level thresholding, but its evaluation becomes computationally costly when extended to perform multilevel thresholding owing to the exhaustive search performed for the optimum threshold values. Therefore, in this paper, an efficient multilevel thresholding technique based on cuckoo search algorithm is adopted to render multilevel minimum cross entropy more practical and reduce the complexity. Experiments have been conducted over different color images including natural and satellite images exhibiting low resolution, complex backgrounds and poor illumination. The feasibility and efficiency of proposed approach is investigated through an extensive comparison with multilevel minimum cross entropy based methods that are optimized using artificial bee colony, bacterial foraging optimization, differential evolution, and wind driven optimization. In addition, the proposed approach is compared with thresholding techniques depending on between-class variance (Otsu) method and Tsalli’s entropy function. Experimental results based on qualitative results and different fidelity parameters depicts that the proposed approach selects optimum threshold values more efficiently and accurately as compared to other compared techniques and produces high quality of the segmented images.     

蜂群优化的二维非对称Tsallis交叉熵图像阈值选取

交叉熵能够度量图像分割前后的差异,与Shannon交叉熵相比,引入参数q的Tsallis交叉熵则为图像阈值分割提供了灵活性和普适性,而非对称Tsallis交叉熵的表达形式更加简洁。由此,提出了蜂群优化的二维非对称Tsallis交叉熵图像阈值选取方法。首先引出了非对称Tsallis交叉熵,导出了二维非对称Tsallis交叉熵阈值选取公式,并利用递推方式计算阈值选取准则函数涉及的中间变量,建立查找表,消除冗余运算;然后采用蜂群算法搜寻最佳二维阈值。大量实验结果表明,相对二维最大Shannon熵法、二维Shannon交叉熵法、二维Tsallis熵法和二维对称Tsallis交叉熵法等同类方法,所提出方法在主观视觉效果和区域间对比度评价指标上有较大的改善,能够更准确地分割出目标,运行速度也更快。