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This paper discusses an efficient pattern matching approach on the use of K-nearest neighbour (K-NN) based rank order reduction and Haar transform in order to detect a pattern in a large scene image. To accomplish the task, scene image is divided into a number of candidate windows and both input pattern and candidate windows are characterised by Haar transform. This characterisation seeks to determine distinctive coefficients known as Haar projection values (HPVs). To obtain more relevant and useful representation of HPVs, rectangle sum is computed and further, sum of absolute (SAD) correlation measure is applied as successive measures between the input pattern and candidate windows. This leads to increase the possibility of finding the object in the scene image before being detected and localised. The proposed pattern matching approach is tested on COIL-100 database and the matching accuracy proves the efficacy of the proposed algorithm.

Summary Pattern matching is a fundamental machine vision problem that deals with searching an object in a comparatively large scene. It can use to solve many vision problems ranging from typical human detection to searching defective parts in industrial automation. This paper reports a fast pattern matching technique which makes use of cumulative subtraction and cumulative division operations based on Image Integral model. The idea is to use both the cumulative subtraction and division operations to evaluate the image values on a very small rectangular region of the image scene as well as on the input pattern to be searched for. Image values are transformed to Haar Projection Values (HPVs) using Haar transform in order to achieve pattern matching on sliding window of the image scene. Computation of HPV needs seven arithmetic operations, including two addition and five subtraction operations, which are found to be same as that of Image Integral technique. Besides, the proposed pattern matching technique is identified as computationally effective in terms of both time and memory.

The objective of pattern matching problem is to find the most similar image pattern in a scene image by matching to an instance of the given pattern. For pattern matching, most distinctive features are computed from a pattern that is to be searched in the scene image. Scene image is logically divided into sliding windows of pattern size, and all the sliding windows are to be checked with the pattern for matching. Due to constant matching between the pattern and the sliding window, the matching process should be very efficient in terms of space, time and impacts due to orientation, illumination and occlusion must be minimized to obtain better matching accuracy. This paper presents a novel local feature descriptor called Multi-variant Local Binary Pattern (MVLBP) for pattern matching process while LBP is considered as base-line technique. The efficacy of the proposed pattern matching algorithm is tested on two databases and proved to be a computationally efficient one.

One of the fundamental tasks of pattern recognition is pattern matching. It is the act of checking for presence of a pattern's constituents within token image to have exact match. For that, most distinctive fiducial features of pattern have to be assessed and searched in the sliding windows of same pattern size formed by logically dividing the token scene image. As huge numbers of sliding windows are to be checked with pattern, pattern matching process should be time efficient and to increase pattern matching accuracy impacts due to illumination, resolution, occlusion and pose variation must be reduced. For pattern matching, this paper presents a novel local feature descriptor, multi variant symmetric local graph structure (MVSLGS) taking into account symmetric local graph structure (SLGS) as precedent approach. The computational adequacy of the proposed approach is tested on two publicly available databases with high matching accuracy, showing its proficiency over the process of pattern matching.

This paper reports a fast pattern matching algorithm which makes use of K-NN (K-nearest neighbor) based rank order reduction approach to detect a pattern or object in a given image efficiently. Initially, the given image is divided into several candidate windows, each of size the input pattern. In the next step, both the input pattern and the candidate windows are characterized by Haar transform. From the characterization, Haar Projection Values (HPV) is determined. Further, rectangle sum on both input pattern and candidate windows is computed using integral image technique. Subsequently, by using sum of absolute difference (SAD) correlation distance between the input pattern and candidate windows is determined. In order to detect the pattern, rank order approach using K-NN is applied to determine the first k number of most similar candidate windows containing the input pattern. To reduce the computational complexity of selecting a perfectly matched window, again sum of absolute differences (SAD) is applied and this leads to select the best match pattern having the total object. Decoupling correlation measures also increase the accuracy of matching pattern. Finally, the input pattern is detected and localized in the given image. The pattern matching accuracy proves the efficacy of the proposed algorithm.