Abstract: In stereo vision application, the disparity between the stereo images allows depth estimation within a scene. Detecting conjugate pair in stereo images is a challenging problem known as the correspondence problem. In this paper, an overview of stereo vision is introduced as well as an efficient algorithm and a simple method in depth estimation. We use in this paper the well known matching algorithm, SURF, and we remove the outliers by applying geometric and epipolar constraints. Unlike most of the researchers, we calculate the depth without disparity map. We used the differences between the inliers points resulting from epipolar constraint to get the maximum and the minimum disparity. The depth can be calculated easily from the minimum and the maximum disparity by taking the mean value. To confirm the results we apply on all methods used in estimating the fundamental matrix. Experimental results show that our algorithm in depth estimation works quite robustly.

Abstract: This paper presents a simple matrix form for degree elevation of interval Bezier curve. The four fixed Kharitonov's polynomials (four fixed Bezier curves) associated with the original interval Bezier curve are obtained. The method is based on the matrix representations of the degree elevation process. The process of degree elevations k times are applied to the four fixed Bezier curves of degree n to obtain the four fixed Bezier curves of degree n+k without changing their shapes. Finally the new interval vertices ?{[ß_i^-,ß_i^+ ]}?_(i=0)^(n+k) of the new interval polygon are obtained from vertices of the new fixed polygons of the four fixed Bezier curves. An illustrative example is included in order to demonstrate the effectiveness of the proposed method.

Abstract: The Set Partitioning in Hierarchical Trees (SPIHT) image compression algorithm is very efficient, has low computational complexity, and generates an embedded compressed bit-stream that can be efficiently decoded at several data rates (qualities). Unfortunately it needs a huge amount of computer memory due to using three linked lists to store the coordinates of the image pixels. In addition the SPIHT has complex memory management due to the random access to these lists. This paper proposes a new algorithm termed Single List-SPIHT (SLS). The proposed SLS algorithm has very low memory requirements as it needs about six times less memory than the original SPIHT. This is achieved by using a single list and two state mark bitmaps instead of the three lists that are used by the original SPIHT. In addition, the proposed SLS has simpler memory management because once a pixel is added to the list, it will never be removed. This will permit to implement the list as a simple ordered array that is accessed sequentially. Moreover, the size of the list can be predefined which avoids the dynamic memory allocation problem. This memory reduction and management simplification make the SLS algorithm very suitable for hardware implementation. Furthermore, SLS has better compression performance as compared to the original SPIHT. The price paid for these features is very slight increment in the algorithm's complexity as compared to the original SPIHT.