Detecting small targets is essential for mitigating the sea based Infrared search and track (IRST) problem. It is easy to detect small targets in homogeneous backgrounds such as the sky. When targets are on the border line of heterogeneous backgrounds such as the horizon in the sky and sea surface, solving the problem of detection becomes difficult. This pa per presents a novel spatial filtering method, called Double Layered-Background Removal Filter (DL-BRF), for achieving high detection rates and low false alarm rates. DL-BRF consists of a Modified-Mean Subtraction Filter (M-MSF) and a consecutive Local-Directional Background Removal Filter (L-DBRF). M-MSF enhances the target signal and reduces background noise. L-DBRF removes horizontal structures, which upgrade the signal-to-clutter ratio and background suppression factor. L-DBRF used after M-MSF enhances the synergistic performance of horizontal target detection. We validate the superior performance of the proposed method via real evaluation tests.

In this paper, we present a new object detection method using codebook and boosted Markov Chain Monte Carlo (MCMC) estimation. It is relatively well detected using adaboost and simple Haar-like features for small objects. However, the detection problem is more difficult when object size becomes larger (over 150 × 150) due to different surface markings and clutter. Codebook-based object representation and boosted MCMC method can detect large objects robustly. Experimental results validate convincing detection for large objects.

Categorizing visual elements is fundamentally important for autonomous mobile robots to get intelligence such as new object acquisition and topological place classification. The main problem of visual categorization is how to reduce the large intra-class variations, especially surface markings of man-made objects. In this paper, we present a robust method by introducing intermediate blurring and entropy-guided codebook selection in a bag-of-words framework. Intermediate blurring can filter out the high frequency of surface markings and provide dominant shape information. Entropy of a hypothesized codebook can provide the necessary measure for the semantic parts among training exemplars. From the first step, a generative optimal codebook for each category is learned using the MDL (minimum description length) principle guided by entropy information. From the second step, a final set of codebook is learned using the discriminative method guided by the inter-category entropy of the codebook. We select the necessary parameters through various evaluations and validate the effect of the surface marking reduction method using a Caltech-101 DB, which has large intra-class variations. Finally, we briefly introduce the impact of the method to the object categorization and segmentation problem

Visual categorization is fundamentally important for autonomous mobile robots to get intelligence such as novel object acquisition and topological place recognition. The main difficulty of visual categorization is how to reduce the large intra-class variations. In this paper, we present a new method made robust to that problem by using intermediate blurring and entropy-guided codebook selection in a bag-of-words framework. Intermediate blurring can reduce the high frequency of surface markings and provide dominant shape information. Entropy of a hypothesized codebook can provide the necessary amount of repetition among training exemplars. A generative optimal codebook for each category is learned using the MDL (minimum description length) principle guided by entropy information. Finally, a discriminative codebook is learned using the discriminative method guided by the inter-category entropy of the codebook. We validate the effect of the proposed method using a Caltech-101 DB, which has large intra-class variations

In this paper, we present a practical place and object recognition method for guiding visitors in building environments. Due to motion blur or camera noise, places or objects can be ambiguous. The first key contribution of this work is the modeling of bidirectional interaction between places and objects for simultaneous reinforcement. The second key contribution is the unification of visual context, including scene context, object context, and temporal context. The last key contribution is a practical demonstration of the proposed system for visitors in a large scale building environment. 1

We present an accurate metric localization method using a simple artificial landmark for the navigation of indoor mobile robots. The proposed landmark model is designed to have a three-dimensional, multi-colored structure and the projective distortion of the structure encodes the distance and heading of the robot with respect to the landmark. Catadioptric vision is adopted for the robust and easier acquisition of the bearing measurements for the landmark. We propose a practical EKF based self-localization method that uses a single artificial landmark and runs in real time.

In this paper, we present a recognition-based autonomous navigation system for mobile robots. The system is based on our previously proposed robust invariant feature (RIF) detector. This detector extracts highly robust and repeatable features based on the key idea of tracking multi-scale interest points and selecting unique representative local structures with the strongest response in both spatial and scale domains. Weighted Zernike moments are used as the feature descriptor and applied to the place recognition. The navigation system is composed of on-line and off-line two stages. In the off-line learning stage, we train the robot in its workspace by just taking several images of representative places as landmarks. Then, in the on-line navigation stage, the robot recognizes scenes, obtains robust feature correspondences, and navigates the environment autonomously using the iterative pose converging (IPC) algorithm which is based on the idea of the visual servoing technique. The experimental results and the performance evaluation show that the proposed navigation system can achieve excellent performance in complex indoor environments.

This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.

We propose a vision based 3D object recognition and tracking system, which provides high level scene descriptions such as object identification and 3D pose information. The system is composed of object recognition part and real-time tracking part. In object recognition, we propose a feature which is robust to scale, rotation, illumination change and background clutter. A probabilistic voting scheme maximizes the conditional probability defined by the features in correspondence to recognize an object of interest. As a result of object recognition, we obtain the homography between the model image and the input scene. In tracking, a Lie group formalism is used to cast the motion computation problem into simple geometric terms so that tracking becomes a simple optimization problem. An initial object pose is estimated using correspondences between the model image and the 3D CAD model which are predefined and the homography which relates the model image to the input scene. Results from the experiments show the robustness of the proposed system.

We present a simple artificial landmark model and a robust tracking algorithm for the navigation of indoor mobile robots. The landmark model is designed to have a three-dimensional structure consisting of a multi-colored planar pattern. A stochastic algorithm based on Condensation [1] tracks the landmark model robustly using the color distribution of the pattern. A new self-localization algorithm computes the location of robot with the tracked single landmark. Experimental results show that the proposed landmark model is eflective. Through extensive navigation experiments in a cluttered indoor environment, we demonstrate the feasibility of the single view based self-localization in real-time.