Algorithms used for classifying visual signals using methods for extracting significant features

Klasifikace vizuálních dat je důležitý úkol v moderním oboru počítačového vidění. Kvalita často používaných neuronových sítí závisí na velkém objemu trénovacích dat. Tato práce prozkoumává alternativní konvekční klasifikační metody, kde je rozměr dat redukován pomocí projekce na prostor významných bodů. Lokální a globální techniky extrakce významných bodů, a to SIFT, SURF, ORB a PCA jsou porovnány. Klasifikace pomocí SVM a Bayeovský Model je prozkoumána na třech datasetech o malém množství trénovacích dat a různé komplexnosti.Classification of visual data is a fundamental task in state-of-the-art computer vision. The efficiency of the commonly used neural networks crucially depends on a large amount of training data. This thesis explores alternative conventional classification methods, where the dimension of the visual data is reduced by projecting the data onto a space of significant features. The local and global feature extraction techniques, namely SIFT, SURF, ORB, and PCA are compared. The classification by SVM and the Bayesian Model is examined on three datasets with a small number of training images of different complexity.470 - Katedra aplikované matematikyvýborn

Plant Seed Identification

Plant seed identification is routinely performed for seed certification in seed trade, phytosanitary certification for the import and export of agricultural commodities, and regulatory monitoring, surveillance, and enforcement. Current identification is performed manually by seed analysts with limited aiding tools. Extensive expertise and time is required, especially for small, morphologically similar seeds. Computers are, however, especially good at recognizing subtle differences that humans find difficult to perceive. In this thesis, a 2D, image-based computer-assisted approach is proposed. The size of plant seeds is extremely small compared with daily objects. The microscopic images of plant seeds are usually degraded by defocus blur due to the high magnification of the imaging equipment. It is necessary and beneficial to differentiate the in-focus and blurred regions given that only sharp regions carry distinctive information usually for identification. If the object of interest, the plant seed in this case, is in- focus under a single image frame, the amount of defocus blur can be employed as a cue to separate the object and the cluttered background. If the defocus blur is too strong to obscure the object itself, sharp regions of multiple image frames acquired at different focal distance can be merged together to make an all-in-focus image. This thesis describes a novel non-reference sharpness metric which exploits the distribution difference of uniform LBP patterns in blurred and non-blurred image regions. It runs in realtime on a single core cpu and responses much better on low contrast sharp regions than the competitor metrics. Its benefits are shown both in defocus segmentation and focal stacking. With the obtained all-in-focus seed image, a scale-wise pooling method is proposed to construct its feature representation. Since the imaging settings in lab testing are well constrained, the seed objects in the acquired image can be assumed to have measureable scale and controllable scale variance. The proposed method utilizes real pixel scale information and allows for accurate comparison of seeds across scales. By cross-validation on our high quality seed image dataset, better identification rate (95%) was achieved compared with pre- trained convolutional-neural-network-based models (93.6%). It offers an alternative method for image based identification with all-in-focus object images of limited scale variance. The very first digital seed identification tool of its kind was built and deployed for test in the seed laboratory of Canadian food inspection agency (CFIA). The proposed focal stacking algorithm was employed to create all-in-focus images, whereas scale-wise pooling feature representation was used as the image signature. Throughput, workload, and identification rate were evaluated and seed analysts reported significantly lower mental demand (p = 0.00245) when using the provided tool compared with manual identification. Although the identification rate in practical test is only around 50%, I have demonstrated common mistakes that have been made in the imaging process and possible ways to deploy the tool to improve the recognition rate

Curve-Based Shape Matching Methods and Applications

One of the main cues we use in our everyday life when interacting with the environment is shape. For example, we use shape information to recognise a chair, grasp a cup, perceive traffic signs and solve jigsaw puzzles. We also use shape when dealing with more sophisticated tasks, such as the medical diagnosis of radiographs or the restoration of archaeological artifacts. While the perception of shape and its use is a natural ability of human beings, endowing machines with such skills is not straightforward. However, the exploitation of shape cues is important for the development of competent computer methods that will automatically perform tasks such as those just mentioned. With this aim, the present work proposes computer methods which use shape to tackle two important tasks, namely packing and object recognition. The packing problem arises in a variety of applications in industry, where the placement of a set of two-dimensional shapes on a surface such that no shapes overlap and the uncovered surface area is minimised is important. Given that this problem is NP-complete, we propose a heuristic method which searches for a solution of good quality, though not necessarily the optimal one, within a reasonable computation time. The proposed method adopts a pictorial representation and employs a greedy algorithm which uses a shape matching module in order to dynamically select the order and the pose of the parts to be placed based on the “gaps” appearing in the layout during the execution. This thesis further investigates shape matching in the context of object recognition and first considers the case where the target object and the input scene are represented by their silhouettes. Two distinct methods are proposed; the first method follows a local string matching approach, while the second one adopts a global optimisation approach using dynamic programming. Their use of silhouettes, however, rules out the consideration of any internal contours that might appear in the input scene, and in order to address this limitation, we later propose a graph-based scheme that performs shape matching incorporating information from both internal and external contours. Finally, we lift the assumption made that input data are available in the form of closed curves, and present a method which can robustly perform object recognition using curve fragments (edges) as input evidence. Experiments conducted with synthetic and real images, involving rigid and deformable objects, show the robustness of the proposed methods with respect to geometrical transformations, heavy clutter and substantial occlusion

Detection of local features invariant to affines transformations

In recent years the use of local characteristics has become one of the dominant approaches to content based object recognition. The detection of interest points is the first step in the process of matching or recognition. A local approach significantly improves and accelerates image retrieval from databases. Therefore a reliable algorithm for feature detection is crucial for many applications. In this thesis we propose a novel approach for detecting characteristic points in an image. Our approach is invariant to geometric and photometric transformations, which frequently appear between scenes viewed in different conditions.We emphasize the problem of invariance to affine transformations. This transformation is particularly important as it can locally approximate the perspective deformations. Previous approaches provide partial solutions to this problem, as not all essential parameters of local features are estimated in an affine invariant way. Our method is truly invariant to affine transformations, which include significant scale changes. An image is represented by a set of extracted points. The interest points are characterized by descriptors, which are computed with local derivatives of the neighborhoods of points. These descriptors together with a similarity measure enable point-to-point correspondences to be established, and as a result, the geometry between images to be computed. In the context of an image database, the descriptors are used to find similar points in thedatabase, and therefore the similar image. The usefulness of our method is confirmed by excellent results for matching and image retrieval. Several comparative evaluations show that our approach provided for larger progress in the context of these applications. In our experiments we use a large set of real images, enabling representative results to be obtained.Une des approches dominantes pour la reconnaissance d'objets est basée sur les caractéristiques locales. La méthode utilise la description locale calculée au voisinage de points d'intérêt. La détection de points d'intérêt est une première étape dans le processus de la mise en correspondance et de la reconnaissance. L'approche par apparences locales a permis d'améliorer et d'accélérer considérablement la recherche d'images dans des bases de données. Dans cette thèse, nous proposons une nouvelle approche pour la détection de points caractéristiques d'une image. Cette approche est invariante aux transformations géométriques et photométriques, qui apparaissent fréquemment entre les images prises dans des conditions différentes. Nous nous concentrons sur le problème d'invariance aux transformations affines. Cette transformation est particulièrement importante parce qu'elle permet de s'affranchir des problèmes de changements perspectives. Les approches précédentes apportent des solutions partielles, car certains paramètres de points d'intérêt ne sont pas estimés de façon invariante aux changements affines. Nous avons proposé une solution générique à ces problèmes. Notre méthode est réellement invariante aux transformations affines, y compris aux changements d'échelle importants. Les images sont caractérisées par des ensembles de descripteurs calculés en des points caractéristiques détectés automatiquement. Une mesure de ressemblance permet d'établir des correspondances entre les points. Ces correspondances sont ensuite utilisées pour calculer la géométrie qui lie les images. Dans le contexte de la recherche d'images les descripteurs sont utilisés pour retrouver des points similaires dans la base et par conséquent des images similaires aux images requêtes. Les résultats expérimentaux pour la mise en correspondance et la recherche d'images montrent que notre approche est très robuste et efficace même dans les cas de changements importants. Plusieurs études comparatives effectuées dans cette thèse montrent l'avantage de cette méthode par rapport aux approches existantes présentées récemment dans la littérature