122 research outputs found
Face Recognition Under Varying Illumination
This study is a result of a successful joint-venture with my adviser Prof. Dr. Muhittin Gökmen. I am thankful to him for his continuous assistance on preparing this project. Special thanks to the assistants of the Computer Vision Laboratory for their steady support and help in many topics related with the project
Comparative Analysis of Illumination Normalizations on Principal Component Analysis Based Feature Extraction for Face Recognition
Principle Component Analysis (PCA) is an appearance-based technique for extraction of feature extraction that is commonly used in computer vision and image processing. This technique suffers from illumination variations, thus knowing which illumination control method to be used in PCA-based face recognition system is very important. This paper applies three selected normalization techniques; Discrete Cosine Transform (DCT), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to normalize face images. PCA was further used to extract features from the normalized face images. Euclidean distance was used to classify extracted features. The best recognition accuracy of 91.84% was obtained in DCT for ORL Database, while the best accuracy of 76% was achieved in DCT for FERET Database. The highest FAR of 0.9 was achieved in DCT for ORL Database, while the highest FAR of 0.5 was obtained in DCT and AHE for FERET Database. The highest FRR of 0.2821 was achieved in CLAHE for ORL Database, while 0.3000 was obtained in AHE for FERET Database. It was concluded that illumination control approaches have predominant effect on PCAâbased facial recognition system. Keywordsâ Adaptive Histogram Equalization, Contrast Adaptive Histogram Equalization, Discrete Cosine Transform Illumination Normalization, Principal Component Analysi
State of the Art in Face Recognition
Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state
Automatic lineament analysis techniques for remotely sensed imagery
Imperial Users onl
Sensory Communication
Contains table of contents on Section 2, an introduction, reports on eleven research projects and a list of publications.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Grant 5 R01 DC00270National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Grant 5 R01 DC00100National Institutes of Health Contract 7 R29 DC00428National Institutes of Health Grant 2 R01 DC00126U.S. Air Force - Office of Scientific Research Grant AFOSR 90-0200U.S. Navy - Office of Naval Research Grant N00014-90-J-1935National Institutes of Health Grant 5 R29 DC00625U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-181
Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies
In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts.
In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name âbccr-segsetâ, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the âbccr-segsetâ dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes.
Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the âbccr-segsetâ dataset.
To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the âbccr-segsetâ dataset collected from the laboratory and the âfieldtrip_can_weedsâ dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods
Advanced methods for relightable scene representations in image space
The realistic reproduction of visual appearance of real-world objects requires accurate computer graphics models that describe the optical interaction of a scene with its surroundings. Data-driven approaches that model the scene globally as a reflectance field function in eight parameters deliver high quality and work for most material combinations, but are costly to acquire and store. Image-space relighting, which constrains the application to create photos with a virtual, fix camera in freely chosen illumination, requires only a 4D data structure to provide full fidelity. This thesis contributes to image-space relighting on four accounts: (1) We investigate the acquisition of 4D reflectance fields in the context of sampling and propose a practical setup for pre-filtering of reflectance data during recording, and apply it in an adaptive sampling scheme. (2) We introduce a feature-driven image synthesis algorithm for the interpolation of coarsely sampled reflectance data in software to achieve highly realistic images. (3) We propose an implicit reflectance data representation, which uses a Bayesian approach to relight complex scenes from the example of much simpler reference objects. (4) Finally, we construct novel, passive devices out of optical components that render reflectance field data in real-time, shaping the incident illumination into the desired imageDie realistische Wiedergabe der visuellen Erscheinung einer realen Szene setzt genaue Modelle aus der Computergraphik fĂŒr die Interaktion der Szene mit ihrer Umgebung voraus. Globale AnsĂ€tze, die das Verhalten der Szene insgesamt als Reflektanzfeldfunktion in acht Parametern modellieren, liefern hohe QualitĂ€t fĂŒr viele Materialtypen, sind aber teuer aufzuzeichnen und zu speichern. Verfahren zur Neubeleuchtung im Bildraum schrĂ€nken die Anwendbarkeit auf fest gewĂ€hlte Kameras ein, ermöglichen aber die freie Wahl der Beleuchtung, und erfordern dadurch lediglich eine 4D - Datenstruktur fĂŒr volle Wiedergabetreue. Diese Arbeit enthĂ€lt vier BeitrĂ€ge zu diesem Thema: (1) wir untersuchen die Aufzeichnung von 4D Reflektanzfeldern im Kontext der Abtasttheorie und schlagen einen praktischen Aufbau vor, der Reflektanzdaten bereits wĂ€hrend der Messung vorfiltert. Wir verwenden ihn in einem adaptiven Abtastschema. (2) Wir fĂŒhren einen merkmalgesteuerten Bildsynthesealgorithmus fĂŒr die Interpolation von grob abgetasteten Reflektanzdaten ein. (3) Wir schlagen eine implizite Beschreibung von Reflektanzdaten vor, die mit einem Bayesschen Ansatz komplexe Szenen anhand des Beispiels eines viel einfacheren Referenzobjektes neu beleuchtet. (4) Unter der Verwendung optischer Komponenten schaffen wir passive Aufbauten zur Darstellung von Reflektanzfeldern in Echtzeit, indem wir einfallende Beleuchtung direkt in das gewĂŒnschte Bild umwandeln
Automatic Spatiotemporal Analysis of Cardiac Image Series
RĂSUMĂ
Ă ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de
dĂ©cĂšs en AmĂ©rique du Nord. Chez lâadulte et au sein de populations de plus en plus jeunes,
la soi-disant Ă©pidĂ©mie dâobĂ©sitĂ© entraĂźnĂ©e par certaines habitudes de vie tels que la mauvaise
alimentation, le manque dâexercice et le tabagisme est lourde de consĂ©quences pour les personnes
affectées, mais aussi sur le systÚme de santé. La principale cause de morbidité et de
mortalitĂ© chez ces patients est lâathĂ©rosclĂ©rose, une accumulation de plaque Ă lâintĂ©rieur des
vaisseaux sanguins à hautes pressions telles que les artÚres coronaires. Les lésions athérosclérotiques
peuvent entraĂźner lâischĂ©mie en bloquant la circulation sanguine et/ou en provoquant
une thrombose. Cela mĂšne souvent Ă de graves consĂ©quences telles quâun infarctus. Outre les
problÚmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la
rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population
pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de
Kawasaki. Il sâagit dâune vasculite aigĂŒe pouvant affecter lâintĂ©gritĂ© structurale des parois des
artĂšres coronaires et mener Ă la formation dâanĂ©vrismes. Dans certains cas, ceux-ci entravent
lâhĂ©modynamie artĂ©rielle en engendrant une perfusion myocardique insuffisante et en activant
la formation de thromboses.
Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectuĂ©s Ă lâaide
dâangiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de
projections radiographiques sont acquises en sĂ©ries suite Ă lâinfusion artĂ©rielle dâun agent de
contraste. Ces images révÚlent la lumiÚre des vaisseaux sanguins et la présence de lésions
potentiellement pathologiques, sâil y a lieu. Parce que les sĂ©ries acquises contiennent de lâinformation
trĂšs dynamique en termes de mouvement du patient volontaire et involontaire (ex.
battements cardiaques, respiration et dĂ©placement dâorganes), le clinicien base gĂ©nĂ©ralement
son interprĂ©tation sur une seule image angiographique oĂč des mesures gĂ©omĂ©triques sont effectuĂ©es
manuellement ou semi-automatiquement par un technicien en radiologie. Bien que
lâangiographie par fluoroscopie soit frĂ©quemment utilisĂ© partout dans le monde et souvent
considĂ©rĂ© comme lâoutil de diagnostic âgold-standardâ pour de nombreuses maladies vasculaires,
la nature bidimensionnelle de cette modalitĂ© dâimagerie est malheureusement trĂšs
limitante en termes de spécification géométrique des différentes régions pathologiques. En effet,
la structure tridimensionnelle des stĂ©noses et des anĂ©vrismes ne peut pas ĂȘtre pleinement
appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de
lâimageur. De plus, la prĂ©sence de lĂ©sions affectant les artĂšres coronaires peut ne pas reflĂ©ter
la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT
Cardiovascular disease continues to be the leading cause of death in North America. In adult
and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by
lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses
on the healthcare system. The primary cause of serious morbidity and mortality for these
patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary
arteries. These lesions can lead to ischemic disease and may progress to precarious blood
flow blockage or thrombosis, often with infarction or other severe consequences. Besides
the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased
stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the
most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis
may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions.
These can hinder the blood flowâs hemodynamics, leading to inadequate downstream
perfusion, and may activate thrombus formation which may lead to precarious prognosis.
Diagnosing these two prominent coronary artery diseases is traditionally performed using
fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective
arterial infusion of a radiodense contrast agent, which reveals the vesselsâ luminal
area and possible pathological lesions. The acquired series contain highly dynamic information
on voluntary and involuntary patient movement: respiration, organ displacement and
heartbeat, for example. Current clinical analysis is largely limited to a single angiographic
image where geometrical measures will be performed manually or semi-automatically by a
radiological technician. Although widely used around the world and generally considered
the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of
this imaging modality is limiting in terms of specifying the geometry of various pathological
regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated
in 2D because their observable features are dependent on the angular configuration of
the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not
reflect the true health of the myocardium, as natural compensatory mechanisms may obviate
the need for further intervention. In light of this, cardiac magnetic resonance perfusion
imaging is increasingly gaining attention and clinical implementation, as it offers a direct
assessment of myocardial tissue viability following infarction or suspected coronary artery
disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic
imaging. This issue predisposes clinicians to laborious manual intervention in order
to align anatomical structures in sequential perfusion frames, thus hindering automation o
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