Automatic human face detection for content-based image annotation

In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour filtering classifier to enhance the accuracy in face detection. The initial experimental benchmark shows the proposed scheme can be efficiently applied for image annotation with higher fidelity

Threshold image target segmentation technology based on intelligent algorithms

This paper briefly introduces the optimal threshold calculation model and particle swarm optimization (PSO) algorithm for image segmentation and improves the PSO algorithm. Then the standard PSO algorithm and improved PSO algorithm were used in MATLAB software to make simulation analysis on image segmentation. The results show that the improved PSO algorithm converges faster and has higher fitness value; after the calculation of the two algorithms, it is found that the improved PSO algorithm is better in the subjective perspective, and the image obtained by the improved PSO segmentation has higher regional consistency and takes shorter time in the perspective of quantitative objective data. In conclusion, the improved PSO algorithm is effective in image segmentation

Nonparametric statistics of image neighborhoods for unsupervised texture segmentation

technical reportIn this paper, we present a novel approach to unsupervised texture segmentation that is based on a very general statistical model of image neighborhoods. We treat image neighborhoods as samples from an underlying, high-dimensional probability density function (PDF). We obtain an optimal segmentation via the minimization of an entropy-based metric on the neighborhood PDFs conditioned on the classification. Unlike previous work in this area, we model image neighborhoods directly without preprocessing or the construction of intermediate features. We represent the underlying PDFs nonparametrically, using Parzen windowing, thus enabling the method to model a wide variety of textures. The entropy minimization drives a level-set evolution that provides a degree of spatial homogeneity. We show that the proposed approach easily generalizes, from the two-class case, to an arbitrary number of regions by incorporating an efficient multi-phase level-set framework. This paper presents results on synthetic and real images from the literature, including segmentations of electron microscopy images of cellular structures

Speeding up the K\"ohler's method of contrast thresholding

K{\"o}hler's method is a useful multi-thresholding technique based on boundary contrast. However, the direct algorithm has a too high complexity-O(N 2) i.e. quadratic with the pixel numbers N-to process images at a sufficient speed for practical applications. In this paper, a new algorithm to speed up K{\"o}hler's method is introduced with a complexity in O(N M), M is the number of grey levels. The proposed algorithm is designed for parallelisation and vector processing , which are available in current processors, using OpenMP (Open Multi-Processing) and SIMD instructions (Single Instruction on Multiple Data). A fast implementation allows a gain factor of 405 in an image of 18 million pixels and a video processing in real time (gain factor of 96).Comment: IEEE CopyrightProceedings of the IEEE International Conference on Image Processing ICIP 201

An improved image segmentation algorithm for salient object detection

Semantic object detection is one of the most important and challenging problems in image analysis. Segmentation is an optimal approach to detect salient objects, but often fails to generate meaningful regions due to over-segmentation. This paper presents an improved semantic segmentation approach which is based on JSEG algorithm and utilizes multiple region merging criteria. The experimental results demonstrate that the proposed algorithm is encouraging and effective in salient object detection

An Adaptive Reversible Image Watermarking Scheme Based on Integer Wavelet Coefficients

[[abstract]]This paper presents an integer wavelet coefficients based reversible image watermarking scheme. A reversible image watermarking approach extracts the embedded watermarks from a watermarked image and recovers the watermarked image to the original image simultaneously. The proposed approach first applies the host image to 3-layered integer wavelet transform. Nine subimages are acquired from the 3-layered integer wavelet transform. Each subimage is then segmented to blocks of size 2LX2L, where L is determined by structure of the subimage. Then, reversible watermarks are embedded into differences between central ordered pixel and other pixels in each block. Largest difference in each block determines the embedded quantity in each difference. Experimental results show that the proposed adaptive block size scheme has higher capacity and quality ratio than previous works.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙