Muhammad Bilal Ahmad

A heuristic approach for finding best focused shape

The most popular shape from focus (SFF) methods in the literature are based on the concept of focused image surface (FIS)-the surface formed by the best focus points. According to paraxial-geometric optics, there is one-to-one correspondence between the shape of an object and the shape of its FIS. Therefore, the problem of three-dimensional (3-D) shape recovery from image focus can be described as the problem of determining the shape of the FIS. The conventional SFF method is inaccurate because of piecewise constant approximation of the FIS. The SFF method based on the FIS has shown better results by exhaustive search of the FIS shape using planar surface approximation at the cost of considerably higher computations. In this paper, search of the FIS shape is presented as an optimization problem, i.e., maximization of the focus measure in the 3-D image volume. The proposed method searches the optimal focus measure in the whole image volume, instead of the small volume as adopted in previous methods. The dynamic programming, instead of the approximation techniques, is used to search the optimal FIS shape. A direct application of dynamic programming on a 3-D data is impractical, because of higher computational complexity. Therefore a fast heuristic model based on dynamic programming is proposed for the search of FIS shape. The shape recovery results of the new method are better than previous methods. The proposed algorithm is significantly faster than the FIS algorithm, but a little slower than the conventional algorithm.

Local threshold and Boolean function based edge detection

Localization of edge points in images is one of the most important starting steps in image processing. Many varied edge detection techniques have been proposed. Different edge detectors present distinct and different responses to the same image, showing different details. This work presents a new approach for edge detection. The actual gray level image is locally thresholded using the local mean value to make a binary image. The binary image is checked for edges by comparing with the known edge like patterns, utilizing Boolean algebra. This approach recognizes nearly all, real edges and edges due to noise. For removing edges due to noise, we adopt another approach. This time the actual image is globally thresholded by the variance value of the image. The two resulting images are logically ANDed to get the final edge map.

Application of Three Dimensional Shape from Image Focus in LCD/TFT Displays Manufacturing

One of the fundamental objectives of computer vision is to reconstruct a three-dimensional (3D) structure of objects from two-dimensional (2D) images. Shape from focus (SFF) is the problem of reconstructing the depth of the scene changing actively the optics of the camera until the point of interest is in focus. The point in focus gives information about its depth through the thin lens Gaussian law. Previous SFF algorithms use 2D window for finding the best focused points. In this paper, we propose to use 3D window for finding the best focused points. The new SFF algorithm is applied on the camera mounted microscope for developing an auto-grinding equipment of LCD color filters. In order to develop thin and bright displays, TFT-LCD is used nowadays. In the manufacturing process of TFT-LCD panels, cells and color filters are put together. The protrusion which is generated by combining the two unequal sizes (difference in microns) of glasses in the construction of LCD panels causes a big problem. Therefore, the protrusion is cut to the proper height before the two glasses are put together. Usually, the number of cells in a typical TFT/LCD display is in millions. The time for the checking and cutting of protrusions greatly affects the cost of manufacturing. We, therefore, propose the use of the proposed SFF algorithms for the said problem for fast and accurate process.

Shape From Focus Using Optimization Technique

Three dimensional (3D) shape recovery using shape from focus (SFP) is presented as an optimization problem, i.e., maximization of the focus measure in the 3D image volume. The whole image volume (sequence) is divided into a number of sub image volumes. The search of 3D shape is made in the sub image volumes using dynamic programming optimization technique. The final depth map is obtained by collecting the depth map of the sub volumes. The new algorithm has considerably decreased the computational complexity by searching the 3D shape in sub image volumes and has shown better results. New 3D focus measure operators are also introduced for higher accuracy at the cost of some computational costs

Shape Registration Based on Modified Chain Codes

Shape descriptions based on the traditional chain codes are very susceptible to small perturbations in the contours of the objects. Therefore, direct matching of traditional chain codes could not be used for image retrieval based on the shape boundaries from the large databases. In this paper, a histogram based chain codes are proposed which could be used for image retrieval. The modified chain codes matching are invariant to translation, rotation and scaling transformations, and have high immunity to noise and small perturbations.

Vehicle Tracking Using Image Processing Techniques

A real time vehicle tracking in image sequences is presented. The moving vehicles are segmented by the method of differential image followed by the process of morphological dilation. The vehicles are recognized and tracked using statistical moments. The straight lines in the moving vehicles are found with the help of Radon transform. The direction of the moving object is calculated from the orientation of the straight lines in the direction of the principal axes of the moving objects. The direction of the moving object and the displacement of the object in the image sequence are used to calculate the velocity of the moving objects.

Fast and accurate 3D shape from focus using dynamic programming optimization technique

The conventional shape from focus (SFF) method for 3D shape recovery from image focus is fast but inaccurate. The SFF method based on the focused image surface (FIS) has shown better results by exhaustive search of the FIS shape using planar surface approximation at the cost of considerably higher computations. We investigate a fast and accurate SFF method. The conventional SFF method is used as a rough estimate for pixels at regular steps in the x and y directions, and this rough estimate is used to search the FIS shape for all pixels between the steps using a dynamic programming optimization technique. The proposed algorithm is very fast and shows comparable results with those of accurate SFF methods.

Modified color co-occurrence matrix for image retrieval

Color correlogram for content-based image retrieval (CBIR) characterizes not only the color distribution of pixels, but also the spatial correlation of pairs of colors. Color not only reflects the material of surface, but also varies considerably with the change of illumination, the orientation of the surface, and the viewing geometry of the camera. The invariance to these environmental factors is not considered in most of the color features in color based CBIR including the color correlogram. However, pixels changed their color with almost same proportions with change of environmental factors. This fact is taken into consideration, and new algorithm is proposed. The color co-occurrence matrix for different spatial distances is defined based on the maximum/minimum of color component between the three components (R,G,B) of a pixel. The proposed algorithm has less number of features, and the change of illumination, etc. is also taken into account.

Optimization technique for three-dimensional shape recovery from image focus

The problem of 3-D shape recovery from image focus can be described as the problem of determining the shape of the focused image surface (FIS)—the surface formed by the best focused points. The shape from focus (SFF) methods in the literature are fast but inaccurate because of the piecewise constant approximation of FIS. The SFF method based on FIS has shown better results by exhaustive search of FIS shape using a planar surface approximation at the cost of a considerably higher number of computations. We present a method to search FIS shape as an optimization problem, i.e., maximization of focus measure in the 3-D image volume. Each image frame in the image volume (sequence) is divided into subimage frames, and the whole image volume is divided into a number of subimage volumes. A rough depth map at only the central pixel of each subimage frame is determined using one of the traditional SFF methods. A few image frames around the image frame, whose image number in the image volume is obtained from the rough depth at the central pixel of subimage frame, are selected for the subimage volumes. The search of FIS shape is now performed in the subimage volumes using a dynamic programming optimization technique. The final depth map is obtained by collecting the depth map of the subimage volumes. The new algorithm considerably decreases the computational complexity by searching FIS shape in subimage volumes and shows better results.

Power-spectrum-based shape matching for MPEG-7

A frequency transform-based statistical method is proposed for shape matching for MPEG-7. Shape description and its corresponding matching algorithm is one of the main concerns in MPEG-7. The normalized frequency transform is invariant to translation and scaling. The image is transformed into frequency domain using Fourier Transform. Two similar images will have same power spectrum. Annular and radial wedge distributions for the power spectra are extracted. The annular and radial wedges can be set arbitrarily. Different statistical features, such as mean and variances are found for the power spectrum of each selected transformed individual feature. The Euclidean or Minkowsky distance of the extracted features are found with respect to the shapes in the database. The minimum distance is the candidate for the matched shape. The simulation results are performed on the test shapes of MPEG-7.