Selection of relevant information to improve Image Classification using Bag of Visual Words

One of the main challenges in computer vision is image classification. Nowadays the number of images increases exponentially every day; therefore, it is important to classify them in a reliable way.The conventional image classification pipeline usually consists on extracting local image features, encoding them as a feature vector and classify them using a previously created model. With regards to feature codification, the Bag of Words model and its extensions, such as pyramid matching and weighted schemes, have achieved quite good results and have become the state of the art methods.The process as mentioned above is not perfect and computers, as well as humans, may make mistakes in any of the steps, causing a performance drop in classification. Some of the primary sources of error on large-scale image classification are the presence of multiple objects in the image, small or very thin objects, incorrect annotations or fine-grained recognition tasks among others.Based on those problems and the steps of a typical image classification pipeline, the motivation of this PhD thesis was to provide some guidelines to improve the quality of the extracted features to obtain better classification results. The contributions of the PhD thesis demonstrated how a good feature selection can contribute to improving the fine-grained classification, and that there would even be no need to have a big training data set to learn the key features of each class and to predict with good results

Selection of relevant information to improve Image Classification using Bag of Visual Words

One of the main challenges in computer vision is image classification. Nowadays the number of images increases exponentially every day; therefore, it is important to classify them in a reliable way.The conventional image classification pipeline usually consists on extracting local image features, encoding them as a feature vector and classify them using a previously created model. With regards to feature codification, the Bag of Words model and its extensions, such as pyramid matching and weighted schemes, have achieved quite good results and have become the state of the art methods.The process as mentioned above is not perfect and computers, as well as humans, may make mistakes in any of the steps, causing a performance drop in classification. Some of the primary sources of error on large-scale image classification are the presence of multiple objects in the image, small or very thin objects, incorrect annotations or fine-grained recognition tasks among others.Based on those problems and the steps of a typical image classification pipeline, the motivation of this PhD thesis was to provide some guidelines to improve the quality of the extracted features to obtain better classification results. The contributions of the PhD thesis demonstrated how a good feature selection can contribute to improving the fine-grained classification, and that there would even be no need to have a big training data set to learn the key features of each class and to predict with good results

Vision-based navigation for autonomous underwater vehicles

This thesis investigates the use of vision sensors in Autonomous Underwater Vehicle (AUV) navigation, which is typically performed using a combination of dead-reckoning and external acoustic positioning systems. Traditional dead-reckoning sensors such els Doppler Velocity Logs (DVLs) or inertial systems are expensive and result in drifting trajectory estimates. Acoustic positioning systems can be used to correct dead-reckoning drift, however they are time consuming to deploy and have a limited range of operation. Occlusion and multipath problems may also occur when a vehicle operates near the seafloor, particularly in environments such as reefs, ridges and canyons, which are the focus of many AUV applications. Vision-based navigation approaches have the potential to improve the availability and performance of AUVs in a wide range of applications. Visual odometry may replace expensive dead-reckoning sensors in small and low-cost vehicles. Using onboard cameras to correct dead-reckoning drift will allow AUVs to navigate accurately over long distances, without the limitations of acoustic positioning systems. This thesis contains three principal contributions. The first is an algorithm to estimate the trajectory of a vehicle by fusing observations from sonar and monocular vision sensors. The second is a stereo-vision motion estimation approach that can be used on its own to provide odometry estimation, or fused with additional sensors in a Simultaneous Localisation And Mapping (SLAM) framework. The third is an efficient SLAM algorithm that uses visual observations to correct drifting trajectory estimates. Results of this work are presented in simulation and using data collected during several deployments of underwater vehicles in coral reef environments. Trajectory estimation is demonstrated for short transects using the sonar and vision fusion and stereo-vision approaches. Navigation over several kilometres is demonstrated using the SLAM algorithm, where stereo-vision is shown to improve the estimated trajectory produced by a DVL