Design and performance evaluation of a pixel cache implemented within application-specific integrated circuits

The application-specific integrated circuit (ASIC) design and the performance of a graphics processor that uses a pipelined-cache with FIFO memory to transfer a 3D pixel array and its z values to the frame buffer in one cycle are described in detail. The functional modules in the graphics processor include: (1) a video refresh converter, (2) a module that combines texture-mapped patterns onto Phong-shaded surfaces, and (3) a bidircctional parallel link between external devices and the frame-buffer modules. Digital differential analyzer (DDA) algorithms and the size of the pixel cache relative to the frame-buffer bandwidth, have been selected for good overall performance. A drawing speed of 8 ns/pixel (32 bits/pixel) or 1.2 million Phong-shaded polygons/s (100-pixel polygons, texture mapped with hidden surface removal) was achieved when 60-ns accesstime single port DRAMs and synchronous DRAMs were used.     

Alpha-cut implemented fuzzy clustering algorithms and switching regressions.

In the fuzzy c-means (FCM) clustering algorithm, almost none of the data points have a membership value of 1. Moreover, noise and outliers may cause difficulties in obtaining appropriate clustering results from the FCM algorithm. The embedding of FCM into switching regressions, called the fuzzy c-regressions (FCRs), still has the same drawbacks as FCM. In this paper, we propose the alpha-cut implemented fuzzy clustering algorithms, referred to as FCMalpha, which allow the data points being able to completely belong to one cluster. The proposed FCMalpha algorithms can form a cluster core for each cluster, where data points inside a cluster core will have a membership value of 1 so that it can resolve the drawbacks of FCM. On the other hand, the fuzziness index m plays different roles for FCM and FCMalpha. We find that the clustering results obtained by FCMalpha are more robust to noise and outliers than FCM when a larger m is used. Moreover, the cluster cores generated by FCMalpha are workable for various data shape clusters, so that FCMalpha is very suitable for embedding into switching regressions. The embedding of FCMalpha into switching regressions is called FCRalpha. The proposed FCRalpha provides better results than FCR for environments with noise or outliers. Numerical examples show the robustness and the superiority of our proposed methods.     

Sine-fitting multiharmonic algorithms implemented by artificial neural networks

A new method designed to perform high-accuracy spectral analysis, based on ADALINE artificial neural networks (ANNs), is proposed. The proposed network is able to accurately calculate the fundamental frequency and the harmonic content of an input signal. The method is especially useful in high-precision digital measurement systems in which periodical signals are involved, i.e. digital watt meters. Most of these systems use spectral analysis algorithms as an intermediate step for the computation of the magnitudes of interest. The traditional spectral analysis methods require synchronous sampling, which introduce limitations to the sampling circuitry. Sine-fitting multiharmonics algorithms resolve the hardware limitations concerning the synchronous sampling but have some limitations with regard to the phase of the array of samples. The new implementation of sine-fitting multiharmonics algorithms based on ANN eliminates these limitations.     

Splatting errors and antialiasing

The paper describes three new results for volume rendering algorithms utilizing splatting. First, an antialiasing extension to the basic splatting algorithm is introduced that mitigates the spatial aliasing for high resolution volumes. Aliasing can be severe for high resolution volumes or volumes where a high depth of field leads to converging samples along the perspective axis. Next, an analysis of the common approximation errors in the splatting process for perspective viewing is presented. In this context, we give different implementations, distinguished by efficiency and accuracy, for adding the splat contributions to the image plane. We then present new results in controlling the splatting errors and also show their behavior in the framework of our new antialiasing technique. Finally, current work in progress on extensions to splatting for temporal antialiasing is demonstrated. We present a simple but highly effective scheme for adding motion blur to fast moving volumes     

Distributed consensus algorithms for merging feature-based maps with limited communication

In this paper we present a solution for merging feature-based maps in a robotic network with limited communication. We consider a team of robots that explore an unknown environment and build local stochastic maps of the explored region. After the exploration has taken place, the robots communicate and build a global map of the environment. This problem has been traditionally addressed using centralized schemes or broadcasting methods. The contribution of this work is the design of a fully distributed approach which is implementable in scenarios with limited communication. Our solution does not rely on a particular communication topology and does not require any central agent, making the system robust to individual failures. Information is exchanged exclusively between neighboring robots in the communication graph. We provide distributed algorithms for solving the three main issues associated to a map merging scenario: establishing a common reference frame, solving the data association, and merging the maps. We also give worst-case performance bounds for computational complexity, memory usage, and communication load. Simulations and real experiments carried out using various vision sensors validate our results.     

Improved algorithms for searching restriction maps.

We present algorithms for searching a DNA restriction enzyme map for a region that best matches a shorter 'probe' map. Our algorithms utilize a new model of map alignments, and extensive experiments prove our model superior to earlier approaches for certain applications. Let M be the number of map sites and P be the number of probe sites. Our first algorithm, which optimizes only over a restricted class of alignments, requires O(MP log P) worst-case time and O(M + P) space. Our second algorithm, which optimizes over all alignments, runs in O(MP3) time and O(M + P2) space, under reasonable assumptions about the distribution of restriction enzyme cleavage sites. Combining the algorithms gives a map-searching method that optimizes over all alignments in O(MP log P) time in practice. The algorithms' effectiveness is illustrated by searches involving a genomic restriction map of Escherichia coli.     

Compositing 3D images with antialiasing and various shading effects

A method is proposed for compositing 3-D images produced by different programs, taking depth order into consideration. The method can add the following effects to composited images: antialiased images with scaling are displayed by a simple algorithm; the algorithm can add shading effects due to various types of light, such as area sources and skylight; and ray tracing can be performed in localized regions, producing realistic results without the computational expense of ray tracing the whole image. In addition to the above processes, such shading effects as fog and texture mapping can be processed with conventional methods. Thus, it becomes possible to display complex scenes with various shading effects, using relatively small computers. The multiscanning method used for antialiasing and a data structure for processing it are described. Examples are presented to demonstrate the capability of the system     

Perception-based fast rendering and antialiasing of walkthroughsequences

We consider accelerated rendering of high quality walkthrough animation sequences along predefined paths. To improve rendering performance, we use a combination of a hybrid ray tracing and image-based rendering (IBR) technique and a novel perception-based antialiasing technique. In our rendering solution, we derive as many pixels as possible using inexpensive IBR techniques without affecting the animation quality. A perception-based spatiotemporal animation quality metric (AQM) is used to automatically guide such a hybrid rendering. The image flow (IF) obtained as a byproduct of the IBR computation is an integral part of the AQM. The final animation quality is enhanced by an efficient spatiotemporal antialiasing which utilizes the IF to perform a motion-compensated filtering. The filter parameters have been tuned using the AQM predictions of animation quality as perceived by the human observer. These parameters adapt locally to the visual pattern velocity     

A low complexity detection method for video data discontinuity implemented on SoC-FPGA by using pixel location prediction scheme

Multimedia Tools and Applications - Image/video processing in real-time is always in high demand for the quality of video. There are several factors which cause the loss of the video content, such...     

A pixel permutation based image encryption technique using chaotic map

Multimedia Tools and Applications - In the last decade, with a rapid increase in multimedia productions, image encryption has become a significant part of information security. The inherent image...     

Polynomial algorithms for subisomorphism of nD open combinatorial maps

Combinatorial maps describe the subdivision of objects in cells, and incidence and adjacency relations between cells, and they are widely used to model 2D and 3D images. However, there is no algorithm for comparing combinatorial maps, which is an important issue for image processing and analysis. In this paper, we address two basic comparison problems, i.e., map isomorphism, which involves deciding if two maps are equivalent, and submap isomorphism, which involves deciding if a copy of a pattern map may be found in a target map. We formally define these two problems for nD open combinatorial maps, we give polynomial time algorithms for solving them, and we illustrate their interest and feasibility for searching patterns in 2D and 3D images, as any child would aim to do when he searches Wally in Martin Handford’s books.     

Stereo Matching with Transparency and Matting

This paper formulates and solves a new variant of the stereo correspondence problem: simultaneously recovering the disparities, true colors, and opacities of visible surface elements. This problem arises in newer applications of stereo reconstruction, such as view interpolation and the layering of real imagery with synthetic graphics for special effects and virtual studio applications. While this problem is intrinsically more difficult than traditional stereo correspondence, where only the disparities are being recovered, it provides a principled way of dealing with commonly occurring problems such as occlusions and the handling of mixed (foreground/background) pixels near depth discontinuities. It also provides a novel means for separating foreground and background objects (matting), without the use of a special blue screen. We formulate the problem as the recovery of colors and opacities in a generalized 3D (x, y, d) disparity space, and solve the problem using a combination of initial evidence aggregation followed by iterative energy minimization.     

The assessment of evolutionary algorithms for analyzing the positional accuracy and uncertainty of maps

Pre-geodetic maps are an important part of our cultural heritage and a potential source of information for historical studies. Historical cartography should be evaluated in terms of precision and uncertainty prior to their use in any application. In the last decade, the majority of papers that address multi-objective optimization employed the concept of Pareto optimality. The goal of Pareto-based multi-objective strategies is to generate a front (set) of nondominated solutions as an approximation to the true Pareto-optimal front. This article proposes a solution for the problems of multi-objective accuracy and uncertainty analysis of pre-geodetic maps using four Pareto-based multi-objective evolutionary algorithms: HVSEA, NSGAII, SPEAII and msPESA. “The Geographic Atlas of Spain (AGE)” by Tomas Lopez in 1804 provides the cartography for this study. The results obtained from the data collected from the kingdoms of Extremadura and Aragon, sheets of maps (54-55-56-57) and (70-71-72-73), respectively, demonstrate the advantages of these multi-objective approaches compared with classical methods. The results show that the removal of 8% of the towns it is possible to obtain improvements of approximately 30% for HVSEA, msPESA and NSGAII. The comparison of these algorithms indicates that the majority of nondominated solutions obtained by NSGAII dominate the solutions obtained by msPESA and HVSEA; however, msPESA and HVSEA obtain acceptable extreme solutions in some instances. The Pareto fronts based on multi-objective evolutionary algorithms (MOEAs) are a better alternative when the uncertainty of map analyzed is high or unknown. Consequently, Pareto-based multi-objective evolutionary algorithms establish new perspectives for analyzing the positional accuracy and uncertainty of maps.     

Music staff removal with supervised pixel classification

This work presents a novel approach to tackle the music staff removal. This task is devoted to removing the staff lines from an image of a music score while maintaining the symbol information. It represents a key step in the performance of most optical music recognition systems. In the literature, staff removal is usually solved by means of image processing procedures based on the intrinsics of music scores. However, we propose to model the problem as a supervised learning classification task. Surprisingly, although there is a strong background and a vast amount of research concerning machine learning, the classification approach has remained unexplored for this purpose. In this context, each foreground pixel is labelled as either staff or symbol. We use pairs of scores with and without staff lines to train classification algorithms. We test our proposal with several well-known classification techniques. Moreover, in our experiments no attempt of tuning the classification algorithms has been made, but the parameters were set to the default setting provided by the classification software libraries. The aim of this choice is to show that, even with this straightforward procedure, results are competitive with state-of-the-art algorithms. In addition, we also discuss several advantages of this approach for which conventional methods are not applicable such as its high adaptability to any type of music score.     

Symmetric keys image encryption and decryption using 3D chaotic maps with DNA encoding technique

In present digital era, multimedia like images, text, documents and videos plays a vital role, therefore due to increase in usage of digital data; there comes high demand of security. Encryption is a technique used to secure and protect the images from unfair means. In cryptography, chaotic maps play an important role in forming strong and effective encryption algorithm. In this paper 3D chaotic logistic map with DNA encoding is used for confusion and diffusion of image pixels. Additionally, three symmetric keys are used to initialize 3D chaos logistic map, which makes the encryption algorithm strong. The symmetric keys used are 32 bit ASCII key, Chebyshev chaotic key and prime key. The algorithm first applies 3D non-linear logistic chaotic map with three symmetric keys in order to generate initial conditions. These conditions are then used in image row and column permutation to create randomness in pixels. The third chaotic sequence generated by 3D map is used to generate key image. Diffusion of these random pixels are done using DNA encoding; further XOR logical operation is applied between DNA encoded input image and key image. Analysis parameters like NPCR, UACI, entropy, histogram, chi-square test and correlation are calculated for proposed algorithm and also compared with different existing encryption methods.

     

Optimization technique by genetic algorithms for international logistics

In recent years, sea and air transport has become a big trend. Getting an optimal solution with minimum costs (freight cost, warehouse cost) under certain constraints (delivery due date etc.) must be sought. Formerly, we made a mathematical formulation of the fundamental case (a single supply site and a single demand site with multiple delivery dates/different delivery quantities) and next we expanded the objective function from considering transportation costs to considering transportation costs and warehouse stock fees. Under certain constraints, a minimum cost was pursued. In this paper, the objective function is expanded to the scheme which considers a reduced cost for the volume of lots. Here, a reduced cost by the discount of volume is also taken into account when multiple lots are transported using the same type of transport. A new selection method, “Multi-step tournament selection method” which is suitable for this problem is devised and utilized in this paper. Numerical examples are examined for the cases in which the discount of volume is considered. Theoretical optimal solution is derived by using genetic algorithm. An application of genetic algorithm to International Logistics is executed before by us. In this paper, the further expansion of constraints is executed and fruitful result is obtained, which contributes to the real management of International Logistics for the better decision making.     

A new weighted pathfinding algorithms to reduce the search time on grid maps

Artificial Intelligence (AI) techniques are utilized widely in the field of Expert Systems (ES) - as applied to robotics, video games self-driving vehicles and so on. Pathfinding algorithms are a class of heuristic algorithms based on AI techniques which are used in ES as decision making functions for the purpose of solving problems that would otherwise require human competence or expertise. ES fields that use pathfinding algorithms and operate in real-time face many challenges: for example time constraints, optimality and memory overhead for storing the paths which are found. For these algorithms to work, appropriate problem-specific maps must be constructed. In relation to this, the uniform-cost grid set-up is the most appropriate for ES applications. In this method, each node in a graph is represented as a tile, and the weight “between” tiles is set at a constant value, usually this is set to 1. In the state-of-the-art heuristic algorithms used with this data structure, multiplying the heuristic function by a weight greater than one is well-known technique. In this paper, we present three new techniques using various weights to accelerate heuristic search of grid maps. The first such technique is based on the iteration of a heuristic search algorithm associated with weight-set w. The second technique is based on the length between the start node and goal node, which is then associated with w. The last technique is based on the travel cost and is associated with a weight-set α. These techniques are applicable to a wide class of heuristic search algorithms. Therefore, we implement them, here, within the A*, the Bidirectional A* (Bi-A*) and Jump Point Search (JPS) algorithms; thus obtaining a family of new algorithms. Furthermore, it is seen that the use of these new algorithms results in significant improvements over current search algorithms. We evaluate them in path-planning benchmarks and show the amended JPS technique's greater stability, across weight values, over the other two techniques. However, it is also shown that this technique yields poor results in terms of cost solution.