Salient Region Detection Methods with Application to Traffic Sign Recognition from Street View Images

• In the computer vision community, saliency detection refers to modeling the selective mechanism in human visual attentions. Outputs of saliency detection algorithms are called saliency maps, which represent conspicuousness levels of different scene areas. Since saliency detection is an effective way to estimate regions of interest that may be attractive to human eyes, numerous applications range from object recognition, image compression, to content-based image editing and image retrieval. This thesis focuses on salient region detection, which aims at detecting and segmenting holistic salient objects from natural images. Despite of many existing models/algorithms and rapid progress in this field over the past decade, improving the detection performance in complex and unconstrained scenarios remains challenging. This thesis proposes five innovative methods for salient region detection. Each method is designed to solve some issues in the existing models. The main contributions of this thesis include: 1) A novel method that induces saliency maps through eigenvectors of the normalized graph cut for better visual clustering of objects and background. It leads to more accurate saliency estimation. 2) A novel data-driven method for salient region detection based on continuous conditional random field (C-CRF). It provides an optimal way to integrate various unary saliency features with pairwise cues. 3) A robust graph-based diffusion method, referred to as manifold-preserving diffusion (MPD). Based on two assumptions on manifold---smoothness and local reconstruction, the method preserves the manifold used in the saliency diffusion. 4) A superpixel-based method that effectively computes color contrast and color distribution attributes of images in a unified manner. 5) A new geodesic propagation method that is used to optimize coarse salient regions for rendering visual coherence. In addition, driven by applications, this thesis also addresses traffic sign recognition (TSR) problem from street view images. As a new application linking between saliency detection and TSR, salient region detection of traffic signs is investigated in order to enhance the sign classification performance.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)

Keyword

normalized cut

traffic sign recognition

saliency propagation

color distribution

continuous conditional random field

manifold

Salient region detection

adaptive graph edge weights

color contrast

geodesics

Publication and Content Type

dok (subject category)

vet (subject category)