- author = "Haythem Ghazouani",
- title = "Image Analysis and Understanding: Application to texture classification, facial expression recognition and breast cancer diagnosis",
- school = "Ecole Nationale d'Ingenieurs de Carthage, Universite de Carthage",
- year = "2023",
- type = "Habilitation Universitaire",
- address = "Tunisia",
- month = "1 " # feb,
- keywords = "genetic algorithms, genetic programming, HL-GP, HOG-LBP, edge detection, tree GP, SVM",
-
URL = "
https://theses.hal.science/tel-03985799",
-
biburl = "https://dblp.org/rec/books/hal/Ghazouani23.bib",
-
URL = "
https://www.researchgate.net/profile/Haythem-Ghazouani/MmoiredeSynthsedeRecherche.pdf",
-
URL = "https://hal-lirmm.ccsd.cnrs.fr/tel-03985799v1",
-
URL = "https://hal-lirmm.ccsd.cnrs.fr/tel-03985799v1/file/M%C3%A9moire%20de%20Synth%C3%A8se%20de%20Recherche.pdf",
- size = "95 pages",
- abstract = "As one of the most active research areas in computer vision, image analysis and understanding attempts to detect low-level and high-level features, to locate, recognize objects, to detect anomalies and to classify them into classes and categories from images and videos. This dissertation focuses on the automation of visual feature extraction, selection and fusion for image classification. The main contribution presented in this manuscript is the fully automation of the process of local feature extraction and aggregation using genetic programming with different applications ranging from texture classification to breast cancer diagnosis including facial expression recognition. More precisely, low-level texture features are defined based on edge arrangements and automatically aggregated for texture image classification under different changes. The same framework is used to extract texture cues from human faces and fuse them with geometric features representing face landmark distances in order to capture wrinkles and face distortions to detect human affects. Facial expression recognition from 3D/4D facial images is also performed based on mesh-local binary pattern difference descriptor representing a unified set of geometric and appearance features of different facial regions. Texture is explored more intensely in breast tissue from mammography images to diagnosis cancer. A more powerful texture description is proposed to detect malignant tumor in breast tissue. A fully automated framework based on genetic programming for feature extraction, selection and fusion is also presented to perform content based retrieval and breast cancer diagnosis. For all the investigated applications, the presented frameworks perform training with small number of instances and tackle the problem of the unavailability of labeled data.",
-
notes = "In english. Also known as
\cite{DBLP:books/hal/Ghazouani23}
HAL Id: tel-03985799
UIUCTex",
