Noise Tolerant Descriptor for Texture Classification

International audienceAmong many texture descriptors, the LBP-based representation emerged as an attractive approach thanks to its low complexity and effectiveness. Many variants have been proposed to deal with several limitations of the basic approach like the small spatial support or the noise sensitivity. This paper presents a new method to construct an effective texture descriptor addressing those limitations by combining three features: (1) a circular average filter is applied before calculating the Complemented Local Binary Pattern (CLBP), (2) the histogram of CLBPs is calculated by weighting the contribution of every local pattern according to the gradient magnitude, and (3) the image features are calculated at different scales using a pyramidal framework. An efficient calculation of the pyramid using integral images, together with a simple construction of the multi-scale histogram based on concatenation, make the proposed approach both fast and memory efficient. Experimental results on different texture classification databases show the good results of the method, and its excellent noise robustness, compared to recent LBP-based methods

Image Retrieval based on Macro Regions

Various image retrieval methods are derived using local features, and among them the local binary pattern (LBP) approach is very famous. The basic disadvantage of these methods is they completely fail in representing features derived from large or macro structures or regions, which are very much essential to represent natural images. To address this multi block LBP are proposed in the literature. The other disadvantage of LBP and LTP based methods are they derive a coded image which ranges 0 to 255 and 0 to 3561 respectively. If one wants to integrate the structural texture features by deriving grey level co-occurrence matrix (GLCM), then GLCM ranges from 256 x 256 and 3562 x 3562 in case of LBP and LTP respectively. The present paper proposes a new scheme called multi region quantized LBP (MR-QLBP) to overcome the above disadvantages by quantizing the LBP codes on a multi-region, thus to derive more precisely and comprehensively the texture features to provide a better retrieval rate. The proposed method is experimented on Corel database and the experimental results indicate the efficiency of the proposed method over the other methods

A REAL-TIME PEDESTRIAN DETECTION SYSTEM IN STREET SCENE

U današnje vrijeme dolazi do sve veće svijesti očuvanju okoliša. Često je na udaru automobilska industrija jer se smatra jednim od najvećih zagađivača okoliša. Iz tih se razloga energetski sustavi sve više okreću prema obnovljivim izvorima energije a transport prema elektrifikaciji putem hibridnih i električnih vozila. Iako je većina komponenti koja sačinjava hibridni električni pogon vozila već odavno poznata te njihov razvoj dolazi do vrhunca, nije još točno određen i definiran pravac najboljeg i najefikasnijeg upravljanja hibridnim pogonima, posebno onima kod kojih je električni motor relativno male snage (tzv. umjerena hibridna vozila). Pod pojmom upravljanja u ovom kontekstu smatra se pojam nadređene strategije upravljanja koji se bavi time kako najbolje iskoristiti komponente hibridnog sustava u svrhu smanjenje potrošnje goriva. Iz tog razloga u ovom radu se obrađuje jedan od mogućih načina upravljanja umjerenim hibridnim električnim vozilom paralelne P2 konfiguracije. Razvija se strategija temeljena na bazi pravila (engl. rule-based), koja ovisno o zadanim pravilima definira stanje i parametre rada zadanih komponenti. Unutar upravljačke strategije definiraju se takozvane funkcionalnosti hibridnog vozila prema prethodno spomenutoj bazi pravila. Funkcionalnosti koje se razmatraju jesu: sporohodna električna vožnja, pasivno i aktivno električno krstarenje te regenerativno kočenje. Razvijena upravljačka strategija implementira se i ispituje unutar simulacijskog paketa AVL CRUISE, pri čemu je kod simulacijskog ispitivanja naglasak na analizi smanjenja potrošnje goriva te utjecaja voznost na vozila.In recent years, environmental awareness is growing. Automotive industry is often hit by criticism as it is considered as one of the largest environmental polluters. For these reasons energy systems are increasingly turning towards renewable energy sources, and transport systems towards electrification through hybrid and electric vehicles. Although most of the components which constitute the hybrid system have long been known and their development is at its climax, the best and most efficient control strategy design for such systems has not yet been defined and determined, especially for those with relatively low power electric motors (so-called mild hybrid electric vehicles). Here, the control strategy term refers to high-level control strategy for hybrid powertrain, which combines the hybrid system components with the aim of reducing the fuel consumption. For this reason, this paper deals with one of the possible approaches of high-level control strategy development for mild hybrid electric vehicle in P2 configuration. A rule-based control strategy is developed, which, depending on the given rules, defines the operation mode and parameters of the hybrid powertrain components. Within the control strategy, the so-called hybrid vehicle functionalities are defined according to the previously mentioned rule base. The functionalities to be considered are: eCreep, eCoasting, eSailing and regenerative braking. The developed control strategy is implemented and verified within the simulation package AVL CRUISE, whereby simulation testing focuses on analysis of fuel consumption reduction and the driveability of the vehicle

Using facial expression recognition for crowd monitoring.

Master of Science in Engineering. University of KwaZulu-Natal, Durban 2017.In recent years, Crowd Monitoring techniques have attracted emerging interest in the eld of computer vision due to their ability to monitor groups of people in crowded areas, where conventional image processing methods would not suffice. Existing Crowd Monitoring techniques focus heavily on analyzing a crowd as a single entity, usually in terms of their density and movement pattern. While these techniques are well suited for the task of identifying dangerous and emergency situations, such as a large group of people exiting a building at once, they are very limited when it comes to identifying emotion within a crowd. By isolating different types of emotion within a crowd, we aim to predict the mood of a crowd even in scenes of non-panic. In this work, we propose a novel Crowd Monitoring system based on estimating crowd emotion using Facial Expression Recognition (FER). In the past decade, both FER and activity recognition have been proposed for human emotion detection. However, facial expression is arguably more descriptive when identifying emotion and is less likely to be obscured in crowded environments compared to body pos- ture. Given a crowd image, the popular Viola and Jones face detection algorithm is used to detect and extract unobscured faces from individuals in the crowd. A ro- bust and efficient appearance based method of FER, such as Gradient Local Ternary Pattern (GLTP), is used together with a machine learning algorithm, Support Vec- tor Machine (SVM), to extract and classify each facial expression as one of seven universally accepted emotions (joy, surprise, anger, fear, disgust, sadness or neutral emotion). Crowd emotion is estimated by isolating groups of similar emotion based on their relative size and weighting. To validate the effectiveness of the proposed system, a series of cross-validation tests are performed using a novel Crowd Emotion dataset with known ground-truth emotions. The results show that the system presented is able to accurately and efficiently predict multiple classes of crowd emotion even in non-panic situations where movement and density information may be incomplete. In the future, this type of system can be used for many security applications; such as helping to alert authorities to potentially aggressive crowds of people in real-time