This paper presents a novel segmentation algorithm based on optimizing histogram multi-level thresholding of images
by employing a variation of particle swarm optimization (PSO) Algorithm which improves the accuracy and the speed of
segmentation based on the conventional PSO algorithm. Entropy has been chosen as the criteria for segmentation based
on the multi-level thresholding. Entropy is input parameter of a fitness function for finding the best segmentation level.
We have to find the optimum thresholding level based on the entropy of different image segments. A new optimization
algorithm that called Hybrid cooperative- comprehensive learning PSO (HCOCLPSO), is used for optimization in this
paper. This algorithm overcomes on common problems of basic variants of PSO, which are curse of dimensionality and
tendency of premature convergence or in other word, getting stuck in local optima. This segmentation technique has
been compared with conventional segmentation based on PSO and genetic algorithm (GA). We presented our
segmentation results to experts. Our subjective measurements by experts show that we can achieve about 80 percents
accuracy which is a better result when compared with conventional PSO and genetic algorithm. In terms of seed we can
achieve much higher performance than two other schemes.
Diabetes can be recognized by features of retina. Automatic retina feature extraction improves the speed of diabetes
diagnosis. The first step in extracting the features is to localize the optic disk. Methods with low accuracy in localizing
the optic disk include area with maximum lightness or the largest area containing pixels with maximum gray levels. A
more accurate method is to find the physical position of blood vessel that passes through optic disk. This paper presents a
fast and accurate algorithm for localizing the optic disk. The process of localization consists of finding the target area,
Optic Disk center and Optic Disk boundaries. Optic Disk boundaries are recognized by our algorithm with %90
accuracy.
This paper investigates retrieval and indexing schemes in pixel domain, and points to the future work on image retrieval schemes. Image retrieval schemes generate indices for images in pixel or compressed domain based on their features in the corresponding domain. These indices are used to retrieve images from a database. The features in pixel domain are extracted from the color, shape or texture characteristics of images. The application of these three methods depends on the characteristic of the image database, and the query image. In the near future the image databases contains the compressed version of images, therefore there will be a high demand for the image retrieval techniques in compressed domain.
The indices obtained from tree-structured vector quantization have two capabilities. First, they can provide image with different resolutions which gives an hierarchical order image based on the closeness of image blocks. Second, image blocks indices, depending on the tree depth, give the characteristics of neighboring pixels of an image. These indices characteristics have ben used in generating a feature vector which shows the image clusters in different resolutions with the capability of giving information about the neighboring pixels characteristics including edges or smooth image areas. This method has been compared with other previous image retrieval scheme based on vector quantization.
One of the requirements for the fast growing technology of multimedia and Internet is image retrieval. A retrieval scheme needs to be efficient, and effective in finding similar images. This requires a robust retrieval scheme against rotation, reflection, translation, scaling, illumination and noise with low computational cost. In this paper a new scheme which overcomes the problems of previous retrieval systems such as sensitivity to illumination, false edges, translation, rotation, noise is introduced. The computational cost of this method is comparable to the previous methods. In this new scheme the image edges will be extracted first, then the edge angles are quantized. Based on correlation between amplitude and phase of neighboring edges the edge orientation correlogram, which is a 2D matrix, is generated. This matrix is normalized and ordered in such a wy that it becomes invariant to rotation, reflection, scaling and translation. This matrix can be used as a feature vector for describing the image and also as an index in image databases. The experimental result shows this new method is superior to other color-based, color-spatial and shape-based indexing schemes.
This paper introduces a new shape based image retrieval scheme. This scheme employs the image edge directional similarity in generating the feature vector. The previous comparable schemes consider the point edge angles without any regard for its neighboring point edges. Those schemes offer some advantages such as translation, scaling and rotation invariant. Their problems are high sensitivity to false edges or high computational barrier, and lack of considering spatial domain correlation in feature vector components. This new scheme has all the advantages of previous scheme, because it employs the edges point angles, but overcomes their drawbacks with considering the point edge directional similarity. This scheme uses two factors in producing the feature vector which improves the retrieval effectivity; the edge point angle and amplitude, and its relation with its neighboring edge points. This scheme is robust against noise, since noise has little effect on similar directional edges, consequently there is no need to do any extra computation to identify the false edges. This results in low computational cost of this scheme in comparison with other similar schemes.
The indices obtained by tree-structured vector quantization (TSVQ) have an interesting property that enables them to give information about the correlation between two image blocks. If two image blocks are highly correlated, they may have an identical index, or the same ancestors. The existence of high inter-block correlation in natural images results in having neighboring blocks with the same genealogy. This characteristic can be used to compress the indices. This paper introduces a novel method to exploit the genealogical relation between the image block indices obtained from a TSVQ. The performance of this scheme in terms of PSNR versus average rate was compared with some other similar image coders. The results show that this scheme has better compression capability in terms of objective and subjective quality over these schemes at bit rates less than 0.3 bpp.
This paper introduces a new scheme for still image compression based on Vector Quantization (VQ). The new scheme first vector quantized the image, then the indices obtained from quantization are compressed and transmitted. The indices are used as a classifier to identify the active areas of the image. The residual of active areas are vector quantized in the second step and the indices generated are transmitted. The advantage of new scheme is to present the active areas of the coded image accurately without overhead requirement. This scheme shows better subjective and objective in comparison with similar VQ schemes.
It is difficult to achieve a good low bit rate image compression performance with traditional block coding schemes such as transform coding and vector quantization, without regard for the human visual perception or signal dependency. These classical block coding schemes are based on minimizing the MSE at a certain rate. This procedure results in more bits being allocated to areas which may not be visually important and the resulting quantization noise manifests as a blocking artifact. Blocking artifacts are known to be psychologically more annoying than white noise when the human visual response is considered. While image adaptive vector quantization (IAVQ) attempts to address this problem for traditional vector quantization (VQ) schemes by exploiting image dependency, it ignores the human visual perception when allocating bits. This paper addresses this problem through a new IAVQ scheme based on the human visual perception. In this method, the input image is partitioned into visual classes and each class, depending on its visual importance, is adaptively or universally encoded. The objective and subjective quality of this scheme has been compared with JPEG and a previously proposed image adaptive VQ scheme. The new scheme subjectively outperforms both schemes at low bit rates.
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