The Link Between Image Segmentation and Image Recognition

A long standing debate in computer vision community concerns the link between segmentation and recognition. The question I am trying to answer here is, Does image segmentation as a preprocessing step help image recognition? In spite of a plethora of the literature to the contrary, some authors have suggested that recognition driven by high quality segmentation is the most promising approach in image recognition because the recognition system will see only the relevant features on the object and not see redundant features outside the object (Malisiewicz and Efros 2007; Rabinovich, Vedaldi, and Belongie 2007). This thesis explores the following question: If segmentation precedes recognition, and segments are directly fed to the recognition engine, will it help the recognition machinery? Another question I am trying to address in this thesis is of scalability of recognition systems. Any computer vision system, concept or an algorithm, without exception, if it is to stand the test of time, will have to address the issue of scalability

Feature Extraction and Classification of the Forewings of Three Moth Species based on Digital Images

The main objective of this research was to find out the possibility to use digital images of forewings in the species identification of codling moth. The suitability of different areas of forewing as identification marks was also determined. Digital RGB images were used to determine the features of forewings of three different Cydia species (Lepidoptera, Tortricidea). The chosen species were Cydia pomonella, Cydia splendana and Cydia strobilella. Text-based descriptions of the visual appearances of the moth species were used in feature selection. Image processing methods were applied on 6 different areas of 12 different forewings. 168 local features were calculated for each area. Features included direct pixel-wise intensity values and spatially filtered values. Stepwise regression was performed in order to reduce the number of features in linear models. The models were tested with linear regression analysis and hierarchical agglomerative clustering. Based on this research, Cydia pomonella can be identified from the two other Cydia species by forewing images. The identification was more reliable when the features of all 6 target areas were included compared to the case that the features of only 3 target areas were included. However, the forewings of Cydia pomonella were separated correctly from the forewings of two other Cydia species with 3 visible areas. Identification of sitting Cydia pomonella can be based on the measured or calculated features in the white-brown veined area in the middle of the forewing and in the bronze coloured oval in the sub marginal area but possibly not in the dark brown stripe in the inner margin of the forewing. To have distinctive features in regression models, it is recommended to use 21 x 21 –sized or 9 x 9 -sized filtered values rather than direct pixel-wise measurements.Tutkimuksessa haluttiin selvittää, voidaanko omenakääriäinen erottaa muista lähilajeista etusiivestä otetun digitaalikuvan avulla. Lisäksi haluttiin selvittää ne etusiiven alueet, joista lajitunnistus kannattaisi tehdä. Tutkimuksessa käytettiin digitaalisia RGB-kuvia kolmen Cydia-lajin lajitunnistukseen. Tutkimukseen valittiin kohdelajiksi omenan tuholainen, Cydia pomonella, sitä ulkonäöltään läheisesti muistuttava Cydia splendana sekä näistä kahdesta ulkonäöltään selvästi erottuva Cydia strobilella. Etusiivistä valittiin alueet, joissa tekstipohjaisen tiedon perusteella sijaitsivat tyypilliset lajituntomerkit. Tutkimukseen otetuista 12 etusiivestä määritettiin 6 aluetta, joista kaikista määritettiin 168 piirrettä. Piirteisiin kuului muun muassa paikallisia pikselikohtaisia intensiteettejä sekä suodatettuja mittaustuloksia. Piirteiden määrän vähentämiseksi käytettiin askeltavan regressioanalyysin algoritmeja. Valittujen piirteiden perusteella muodostettiin lineaarisia malleja, jotka testattiin lineaarisella regressioanalyysillä ja hierarkisella kokoavalla ryvästyksellä. Tutkimuksen perusteella Cydia pomonella –laji pystytään erottamaan kahdesta muusta Cydia-suvun lajista etusiivistä otettujen digitaalikuvien perusteella. Kaikkien kolmen Cydia –suvun lajin lajitunnistus oli luotettava, kun kuvien 6 tutkittua aluetta otettiin mukaan analyyseihin. Cydia pomonellan etusiivet pystyttiin erottamaan kahdesta muusta Cydia –suvun lajin etusiivistä myös vain kolmen alueen perusteella. Lajitunnistus kannattaa tehdä siiven keskiosan juovikkaan alueen sekä siiven päädyssä olevan pronssinvärisen ovaalin alueen perusteella, mutta luultavasti ei siiven keskiosan tummanruskean viirun perusteella. Erottelevimmat piirteet saatiin 21 x 21 ja 9 x 9 –kokoisilla suotimilla suodatetuista alueista, jotka selittivät paremmin lajien välistä eroa kuin pikselikohtaiset intensiteetit

Combining Regions and Patches for Object Class Localization

We introduce a method for object class detection and localization which combines regions generated by image segmentation with local patches. Region-based descriptors can model and match regular textures reliably, but fail on parts of the object which are textureless. They also cannot repeatably identify interest points on their boundaries. By incorporating information from patch-based descriptors near the regions into a new feature, the Region-based Context Feature (RCF), we can address these issues. We apply Region-based Context Features in a semi-supervised learning framework for object detection and localization. This framework produces object-background segmentation masks of deformable objects. Numerical results are presented for pixel-level performance

Combining regions and patches for object class localization

International audienceWe introduce a method for object class detection and localization which combines regions generated by image segmentation with local patches. Region-based descriptors can model and match regular textures reliably, but fail on parts of the object which are textureless. They also cannot repeatably identify interest points on their boundaries. By incorporating information from patch-based descriptors near the regions into a new feature, the Region-based Context Feature (RCF), we can address these issues. We apply Region-based Context Features in a semi-supervised learning framework for object detection and localization. This framework produces object-background segmentation masks of deformable objects. Numerical results are presented for pixel-level performance

Combining Regions and Patches for Object Class Localization

We introduce a method for object class detection and localization which combines regions generated by image segmentation with local patches. Region-based descriptors can model and match regular textures reliably, but fail on parts of the object which are textureless. They also cannot repeatably identify interest points on their boundaries. By incorporating information from patch-based descriptors near the regions into a new feature, the Region-based Context Feature (RCF), we can address these issues. We apply Region-based Context Features in a semi-supervised learning framework for object detection and localization. This framework produces object-background segmentation masks of deformable objects. Numerical results are presented for pixel-level performance. 1