184 research outputs found
An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
No full textCell nuclei segmentation is a challenging task, especially in real applications, when the target images significantly differ between them. This task is also challenging for methods based on convolutional neural networks (CNNs), which have recently boosted the performance of cell nuclei segmentation systems. However, when training data are scarce or not representative of deployment scenarios, they may suffer from overfitting to a different extent, and may hardly generalise to images that differ from the ones used for training. In this work, we focus on real-world, challenging application scenarios when no annotated images from a given dataset are available, or when few images (even unlabelled) of the same domain are available to perform domain adaptation. To simulate this scenario, we performed extensive cross-dataset experiments on several CNN-based state-of-the-art cell nuclei segmentation methods. Our results show that some of the existing CNN-based approaches are capable of generalising to target images which resemble the ones used for training. In contrast, their effectiveness considerably degrades when target and source significantly differ in colours and scale
Detection of red and white blood cells from microscopic blood images using a region proposal approach
No full textIn this paper, we propose a novel and efficient method for detecting and quantifying red and white blood cells from microscopic blood images. Laboratory tests that use a cell counter or a flow cytometer can perform a complete blood count (CBC) rapidly. Nonetheless, a manual blood smear inspection is still needed, both to have a human check on the counter results and to monitor patients under therapy. Moreover, it allows for describing the cells' appearance as well as any abnormalities. However, manual analysis is lengthy and repetitive, and its result can be subjective and error-prone. In contrast, by using image processing techniques, the proposed system is entirely automated. The main effort is devoted to both achieving high accuracy and finding a way to overcome the typical differences in the condition of blood smear images that computer-aided methods encounter. It is based on the Edge Boxes method, which is considered a state-of-art region proposal approach. By incorporating knowledge-based constraints into the detection process using Edge Boxes, we can find cell proposals rapidly and efficiently. We tested the proposed approach on the Acute Lymphoblastic Leukaemia Image Database (ALL-IDB), a well-known public dataset proposed for leukaemia detection, and the Malaria Parasite Image Database (MP-IDB), a recently proposed dataset for malaria detection. Experimental results were excellent in both cases, outperforming the state-of-the-art on ALL-IDB and creating a strong baseline on MP-IDB, demonstrating that the proposed method can work well on different datasets and different types of images
Camp. (Oristano) [kanʧɔfˈfai̯]: una ipotesi etimologica
No full textThis chapter takes into consideration the meaning of verbs derived from the lexical basis [kanˈʧɔffa] ‘artichoke’ (Cynara cardunculus L. var. Scolymus)’ in a variety of Campidanese Sardinian (Oristano). In particular, the article examines the verb [kanʧɔfˈfai̯], semantically ‘to deceive intentionally’, so far not attested in dictionaries. The etymological motivation of this meaning is hypothesized, relating it to the consumption and trade of wines. Tannins in artichokes create a strong sense of bitterness which alter the perception of taste, and because of this artichokes in Sardinia were consciously linked to the consumption of particularly acidic wine. It is therefore hypothesised that the verb [kanʧɔfˈfai̯] may have shifted from indicating a deceptive practice in the food trade, especially regarding wine,
carried out at the expense of a naïve person who is unaware of a highly widespread notion, to one generically associated to serious fraud
Human-in-the-loop cross-domain person re-identification
No full textPerson re-identification is a challenging cross-camera matching problem, which is inherently subject to domain shift. To mitigate it, many solutions have been proposed so far, based on four kinds of approaches: supervised and unsupervised domain adaptation, direct transfer, and domain generalisation; in particular, the first two approaches require target data during system design, respectively labelled and unlabelled. In this work, we consider a very different approach, known as human-in-the-loopHITL), which consists of exploiting user’s feedback on target data processed during system operation to improve re-identification accuracy. Although it seems particularly suited to this application, given the inherent interaction with a human operator, HITL methods have been proposed for person re-identification by only a few works so far, and with a different purpose than addressing domain shift. However, we argue that HITL deserves further consideration in person re-identification, also as a potential alternative solution against domain shift. To substantiate our view, we consider simple HITL implementations which do not require model re-training or fine-tuning: they are based on well-known relevance feedback algorithms for content-based image retrieval, and of novel versions of them we devise specifically for person re-identification. We then conduct an extensive, cross-data set experimental evaluation of our HITL implementations on benchmark data sets, and compare them with a large set of existing methods against domain shift, belonging to the four categories mentioned above. Our results provide evidence that HITL can be as effective as, or even outperform, existing ad hoc solutions against domain shift for person re-identification, even under the simple implementations we consider. We believe that these results can foster further research on HITL in the person re-identification field, where, in our opinion, its potential has not been thoroughly explored so far
On the effectiveness of synthetic data sets for training person re-identification models
No full textPerson re-identification is a prominent topic in computer vision due to its security-related applications, and to the fact that issues such as variations in illumination, background, pedestrian pose and clothing appearance make it a very challenging task in real-world scenarios. State-of-the-art supervised methods require a huge manual annotation effort for training data and exhibit limited generalisation capability to unknown target domains. Synthetic data sets have recently been proposed as one possible solution to mitigate these problems, aimed at improving generalisation capability by encompassing a larger amount of variations in the above mentioned visual factors, with no need for manual annotation. However, existing synthetic data sets differ in many aspects, including the number of images, identities and cameras, and in their degree of photorealism, and there is not yet a clear understanding of how all such factors affect person re-identification performance. This work makes a first step towards filling this gap through an in-depth empirical investigation, where we use existing synthetic data sets for model training and real benchmark ones for performance evaluation. Our results provide interesting insights towards developing effective synthetic data sets for person re-identification
Scene-specific crowd counting using synthetic training images
No full textCrowd counting is a computer vision task on which considerable progress has recently been made thanks to convolutional neural networks. However, it remains a challenging task even in scene-specific settings, in real-world application scenarios where no representative images of the target scene are available, not even unlabelled, for training or fine-tuning a crowd counting model. Inspired by previous work in other computer vision tasks, we propose a simple but effective solution for the above application scenario, which consists of automatically building a scene-specific training set of synthetic images. Our solution does not require from end-users any manual annotation effort nor the collection of representative images of the target scene. Extensive experiments on several benchmark data sets show that the proposed solution can improve the effectiveness of existing crowd counting methods
On The Potential of Image Moments for Medical Diagnosis
No full textMedical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques
Automatic Myelofibrosis Grading from Silver-Stained Images
No full textHistopathology studies the tissues to provide evidence of a disease, type and grade. Usually, the interpretation of these tissue specimens is performed under a microscope by human experts, but since the advent of digital pathology, the slides are digitised, shared and viewed remotely, facilitating diagnosis, prognosis and treatment planning. Furthermore, digital slides can be analysed automatically with computer vision methods to provide diagnostic support, reduce subjectivity and improve efficiency. This field has attracted many researchers in recent years who mainly focused on the analysis of cells morphology on Hematoxylin & Eosin stained samples. In this work, instead, we focused on the analysis of reticulin fibres from silver stained images. This task has been addressed rarely in the literature, mainly due to the total absence of public data sets, but it is beneficial to assess the presence of fibrotic degeneration. One of them is myelofibrosis, characterised by an excess of fibrous tissue. Here we propose an automated method to grade myelofibrosis from image patches. We evaluated different Convolutional Neural Networks for this purpose, and the obtained results demonstrate that myelofibrosis can be identified and graded automatically
Le vicende storiche della sede dell’Accademia di San Luca al Foro di Cesare
No full textLa breve vita dell’incompiuto palazzo dell’Accademia di S. Luca, la cui costruzione venne iniziata nel 1931, è indissolubilmente legata alla lunga storia del Foro di Cesare, sulle cui spoglie si erge, e della chiesa dei Ss. Luca e Martina, sul retro della quale sorse la prima sede storica dell’Accademia realizzata al tempo di papa Urbano VIII (1635-44).
Il nuovo palazzo dell’Accademia dei pittori del quale ancora si conservano le fondazioni riportate alla luce negli scavi condotti dalla Sovraintendenza Comunale di Roma negli anni 1998-2000, doveva infatti essere eretto sul medesimo sito occupato dalla precedente sede. L’importanza dell’edificio è legata, oltre che alla sua prestigiosa ubicazione, anche agli illustri progettisti che furono personalità di spicco del panorama culturale del periodo a cavallo tra gli anni Venti e Trenta dello scorso secolo. I resti del palazzo e i progetti di seguito analizzati sono infatti una eloquente testimonianza tanto dei nuovi fermenti intellettuali quanto delle criticità dell’epoca e palesano pensieri e ripensamenti che furono sottesi alla realizzazione oltre che dell’Accademia stessa anche, e soprattutto, della nascente via dell’Impero.The seat of the ancient, prestigious Academy of San Luca, the leading cultural institution in Roma, was traditionally located by the church of SS. Luca e Martina in the Roman Forum. The first building, which housed the Academy of Painters, was destroyed in 1929-31 when extensive demolitions were carried out in the area, as part of a plan for recovery of the Forum complex. Projects for the new building were submitted by Tullio Passarelli, Gustavo Giovannoni and finally Arnaldo Foschini; after a long debate construction started in 1932, but the project was soon abandoned as the archaeological issue prevailed, and it was decided to excavate the underlying remain of Caesar’s Forum. Passarelli’s and Foschini’s drawings are published here, together with surveys of the present state by the author of the article, and show careful planning in the layout of the interiors, while the exteriors are a remarkable exercise in the academic style, universally adopted in Rome’s official buildings in the Thirties
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