Strategi Region Merging Berdasarkan Pengukuran Fuzzy Similarity pada Segmentasi Citra

Metode segmentasi citra semi otomatis dilakukan dengan cara membagi citra menjadi beberapa region berdasarkan nilai kemiripan antar fitur-fiturnya. Kemudian pengguna memberikan tanda pada beberapa region sebagai sample dari region objek dan background. Selanjutnya sample region tersebut digunakan pada proses region merging terhadap region yang belum ditandai berdasarkan nilai kemiripannya. Beberapa region pada citra memiliki nilai informasi yang tidak merata, seperti blurred contours, soft color shades, dan brightness. Region tersebut pada penelitian ini kita sebut sebagai ambiguous region. Ambiguous region menimbulkan permasalahan pada proses region merging dikarenakan region tersebut memiliki dua nilai informasi yaitu sebagai objek dan background. Hal tersebut dapat menimbulkan kesalahan dalam proses segmentasi. Pada penelitian ini diusulkan strategi region merging baru berdasarkan pengukuran fuzzy similarity pada segmentasi citra. Metode yang diusulkan memiliki empat tahapan, tahap pertama adalah region splitting yang digunakan untuk mendapatkan intial segmentasi. Tahap kedua adalah penandaan manual yang dilakukan oleh pengguna untuk menandai sample dari region objek dan background (user marking). Tahap ketiga adalah initial fuzzy region untuk mendapat inisial seed background dan objek. Tahap terakhir adalah proses region merging menggunakan pengukuran fuzzy similarity dengan memperhitungkan intensitas gray level dan fungsi keaangotaan. Berdasarkan hasil uji coba metode yang diusulkan berhasil melakukan segmentasi pada citra natural dan citra gigi dengan rata-rata nilai misclassification error 1.96% untuk citra natural dan 5.47 % untuk citra gigi. Selain itu metode yang diusulkan dapat menghasilkan segmentasi yang lebih akurat dibandingkan dengan metode MSRM, Global FSM, dan Semi FSM. ======================================================================================= Semi-automatic method of image segmentation can be done by dividing the image into multiple regions based on the similarity between its features. Then the user gives marks on several regions as a sample of the object region and background region. Furthermore, the sample used in the process of region merging between non-marker regions based on their similarity. Some regions of the image have an unbalance information, such as blurred contours, soft color shades, and brightness. We call those regions as ambiguous region. Ambiguous region cause problems during the process of merging because that region has double information as object and background. This can lead to segmentation error. Therefore, we proposed new region merging strategy based on fuzzy similarity measurement on image segmentation. The proposed method has four stages; the first stage is region splitting used to get the initial segmentation. The second stage is manual marking by the user to get a sample of the object region and background. The third stage is determining the initial fuzzy region to receive initial seed background and object. The last stage is the process of merging region against non-marker region by determining the optimal threshold to the cluster background region and object region using fuzzy similarity measurement taking into account the gray level intensity and membership function. The proposed method is expected to optimize image segmentation result than other region merging methods. Experimental results demonstrated that the proposed method can be done segmentation for natural and teeth image with the average value of misclassification error (ME) 1.96% and 5.47% respectively. The proposed method can give accurate segmentation result compared with MSRM, Global FSM, and Semi FSM

Visual region understanding: unsupervised extraction and abstraction

The ability to gain a conceptual understanding of the world in uncontrolled environments is the ultimate goal of vision-based computer systems. Technological societies today are heavily reliant on surveillance and security infrastructure, robotics, medical image analysis, visual data categorisation and search, and smart device user interaction, to name a few. Out of all the complex problems tackled by computer vision today in context of these technologies, that which lies closest to the original goals of the field is the subarea of unsupervised scene analysis or scene modelling. However, its common use of low level features does not provide a good balance between generality and discriminative ability, both a result and a symptom of the sensory and semantic gaps existing between low level computer representations and high level human descriptions. In this research we explore a general framework that addresses the fundamental problem of universal unsupervised extraction of semantically meaningful visual regions and their behaviours. For this purpose we address issues related to (i) spatial and spatiotemporal segmentation for region extraction, (ii) region shape modelling, and (iii) the online categorisation of visual object classes and the spatiotemporal analysis of their behaviours. Under this framework we propose (a) a unified region merging method and spatiotemporal region reduction, (b) shape representation by the optimisation and novel simplication of contour-based growing neural gases, and (c) a foundation for the analysis of visual object motion properties using a shape and appearance based nearest-centroid classification algorithm and trajectory plots for the obtained region classes. 1 Specifically, we formulate a region merging spatial segmentation mechanism that combines and adapts features shown previously to be individually useful, namely parallel region growing, the best merge criterion, a time adaptive threshold, and region reduction techniques. For spatiotemporal region refinement we consider both scalar intensity differences and vector optical flow. To model the shapes of the visual regions thus obtained, we adapt the growing neural gas for rapid region contour representation and propose a contour simplication technique. A fast unsupervised nearest-centroid online learning technique next groups observed region instances into classes, for which we are then able to analyse spatial presence and spatiotemporal trajectories. The analysis results show semantic correlations to real world object behaviour. Performance evaluation of all steps across standard metrics and datasets validate their performance

Path-Based Colour Image Segmentation

The 2 pass raster segmenter is simple, fast and is often quoted in the literature. Unfortunately, it tends to oversegment images even in the presence of small amounts of noise. In this paper we present a generalization of this approach where we discover regions by taking multiple random paths through an image. This approach fares better but still over segments an image. Yet, an analysis of region density shows that the underlying image structure can be discovered from the path based segmentation. Indeed, the discovered edges are comparable to those discovered by the widely used mean shift algorithm

Fuzzy Homogeneity Measures for Path-based Colour Image Segmentation

In this paper we study different measures of path homogeneity for fuzzy path-based image segmentation. We provide fuzzy semantics for the concept of homogeneity in two steps: first, we introduce a fuzzy interpretation of resemblance between feature vectors characterizing neighbor pixels; then, we obtain the homogeneity of a path by aggregating the set of fuzzy resemblances between consecutive pixels in the path. We propose a set of intuitive properties that any suitable aggregation function should verify for this purpose, and we show that these properties are verified by certain families of t-norms. To determine the performance of the proposed functions, a set of experiments is carried out with both synthetic and real images. Finally, the homogeneity functions are used to obtain fuzzy regions in natural images