Jigsaw Puzzle Solver to Locate Piece Position Jigsaw puzzles present an interesting form of problem solving based around techniques of matching and feature recognition. This paper presents a method to find the location of jigsaw pieces relative to the completed puzzle using an ORB feature detector alongside a FLANN based matcher. An image or frame containing a jigsaw piece is reduced to its region of interest through thresholding and contour finding before being passed through the feature matching process. Matches are then filtered using Lowexe2x80x99s ratio test and outliers are found and removed using a homography transformation matrix. Evaluation of the proposed method saw 81.2% accuracy of a jigsaw puzzle with 24 pieces which was significantly more accurate than shape detecting methods and more robust than methods which required specialised jigsaw pieces.

Evaluation of Local Features for NearUniform Scene Images Image stitching requires accurate matching of visual features to achieve good alignment. However, featurebased matching often has poor result particularly when image content is fairly nearuniform and thus it remains a challenging problem to be addressed. When the current stateoftheart feature detectors unable to detect sufficient reliable corresponding keypoints, the output stitched images often suffer from misalignment, projective distortion and visible artefact. This paper presents a new experimental evaluation using especially nearuniform images for the performance of some wellknown feature detectors, such as Harris, SIFT, SURF, BRISK and KAZE. In addition, we have also introduced RCS o  score to compare spatial distribution of the correct matched keypoints in overlapping region between images. The results show that the best performed local feature detector is KAZE. However, none of the tested feature detectors can reach more than 50% spread of the overlapping region.