Ranipa

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Linem ( A Board Game )

Artificial Intelligence, Android, Gaming 

Download Game from Android Market

Linem is a two player board game played on N x N square Board, where each player is given X number of different color pieces for placing on the square board. The objective of the game is to arrange X of one’s own pieces of the same color next to each other vertically, horizontally, or diagonally before one's opponent can do so.

Usually the game begins with placing mode, where each player takes turn by placing their colored pieces in empty cells of the square board. After each player places X pieces on the board, in each of next turn players must move one of their piece to an adjacent empty cell. Any direction left-right, up-down, or diagonal, is permitted for the moves.

Artificial Intelligence

The AI engine of the game is based on very famous minmax algorithm. We worked hard to make CPU to play more like human. Fortunately, we succeeded at a large extend in doing so. The most challenging part was to come up with the heuristic evaluation function to speed up the process of finding the next move.

Screens

Click here to download full game

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A practical approach for depth estimation and image restoration using defocus cue

Computer Vision, Machine Learning, Image Processing

Reconstruction of depth from 2D images is an important research issue in computer vision. Depth from defocus (DFD) technique uses space varying blurring of an image as a cue in reconstructing the 3D structure of a scene. In this project we explored the regularization based approach for simultaneous estimation of depth and image restoration from defocused observations. We are given two defocused observations of a scene that are captured with different camera parameters. Our method consists of two steps. First we obtain the initial estimates for the depth as well as for the focused image. In the second step we refine the solution by using a fast optimization technique. Here we use the classic depth recovery method due to Subbarao for obtaining the initial depth map and Weiner filter approach for initial image restoration. Since the problem we are solving is ill-posed and does not yield unique solution, it is necessary to regularize the solution by imposing additional constraint to restrict the solution space. The regularization is performed by imposing smoothness constraint only. However, for preserving the depth and image intensity discontinuities, they are identified prior to the minimization process from initial estimates of the depth map and the restored image. The final solution is obtained by using computationally efficient gradient descent algorithm, thus avoiding the need for computationally taxing algorithms. The depth as well as intensity edge details of the final solution correspond to those obtained using the initial estimates. The experimental results indicate that the quality of the restored image is good even under severe space-varying blur conditions.

Programming Tools: MatLab, C

Click here to read full paper